Papers with BLEU points
What do Large Language Models Learn about Scripts? (2022.starsem-1)
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| Challenge: | Script Knowledge is important for language understanding but expensive to produce manually and difficult to induce from text due to reporting bias. |
| Approach: | They propose a pipeline-based script induction framework which can generate good quality ESDs for unseen scenarios. |
| Outcome: | The proposed framework produces good quality ESDs for unseen scenarios, but manual evaluation shows there is room for improvement. |
Two-Headed Monster and Crossed Co-Attention Networks (2020.aacl-srw)
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| Challenge: | a new co-attentional neural structure is proposed for machine translation tasks . a higher-level and more abstract paradigm generalized from CCNs is proposed . |
| Approach: | They propose a paradigm that consists of two symmetric encoder modules and one decoder module connected with co-attention. |
| Outcome: | The proposed model outperforms the current Transformer model on translation tasks but the epoch time increases by circa 75%. |
AMR-To-Text Generation with Graph Transformer (2020.tacl-1)
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| Challenge: | Abstract meaning representation (AMR)-to-text generation is challenging task for natural language processing. |
| Approach: | They propose a graph-to-sequence model that directly encodes AMR graphs and learns node representations. |
| Outcome: | The proposed model outperforms the current state-of-the-art neural approach by 1.5 BLEU points on LDC2015E86 and 4.8 BLUE points on the LDC2017T10 and achieves new state- of-the art performance. |
Fully Non-autoregressive Neural Machine Translation: Tricks of the Trade (2021.findings-acl)
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| Challenge: | Existing non-autoregressive neural machine translation models are slow to learn the dependency between output tokens. |
| Approach: | They propose to use fully non-autoregressive neural machine translation (NAT) to predict tokens with single forward of neural networks. |
| Outcome: | The proposed model achieves state-of-the-art results on three translation benchmarks with comparable performance to autoregressive and iterative NAT systems. |
Facebook AI’s WAT19 Myanmar-English Translation Task Submission (D19-52)
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Peng-Jen Chen, Jiajun Shen, Matthew Le, Vishrav Chaudhary, Ahmed El-Kishky, Guillaume Wenzek, Myle Ott, Marc’Aurelio Ranzato
| Challenge: | Using back-translation, we can improve generalization by using noisy channel re-ranking and ensembling. |
| Approach: | They propose to use BPE-based transformer models to leverage monolingual data to improve generalization and use noisy channel re-ranking and ensembling to improve results. |
| Outcome: | The proposed system improves on the baseline system trained exclusively on the provided small parallel dataset, and the human evaluation and BLEU score are higher. |
Unsupervised Pivot Translation for Distant Languages (P19-1)
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| Challenge: | Unsupervised neural machine translation (NMT) is a popular method for transferring information between languages. |
| Approach: | They propose an unsupervised pivot translation method which translates a language to a distant language through multiple hops. |
| Outcome: | The proposed method improves translation on 20 languages and 294 distant languages on 20 different languages and language pairs. |
Adaptively Scheduled Multitask Learning: The Case of Low-Resource Neural Machine Translation (D19-56)
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| Challenge: | Neural Machine Translation suffers from the lack of bilingual data in low-resource scenarios. |
| Approach: | They propose to inject inductive biases into Neural Machine Translation (NMT) using auxiliary syntactic and semantic tasks. |
| Outcome: | The proposed approach improves translation quality by reweighing training data of main and auxiliary tasks based on their contributions to generalisability of main task. |
Few-Shot NLG with Pre-Trained Language Model (2020.acl-main)
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| Challenge: | Neural-based approaches to natural language generation are data-hungry and difficult to adopt in real-world applications. |
| Approach: | They propose a task of few-shot natural language generation from structured data or knowledge to generate coherent sentences from input data and language modeling to compose coherent sentences. |
| Outcome: | The proposed approach outperforms the strongest baseline approach by over 8.0 BLEU points improvement. |
An Effective Approach to Unsupervised Machine Translation (P19-1)
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| Challenge: | a recent research line has managed to train both unsupervised and unsupervised machine translation systems using monolingual corpora only. |
| Approach: | They propose to use monolingual corpora to train both unsupervised and unsupervised machine translation systems. |
| Outcome: | The proposed system achieves 22.5 BLEU points in English-to-German WMT 2014, 5.5 points more than the previous best unsupervised system, and 0.5 points more in the (supervised) shared task winner back in 2014. |
Multi-split Reversible Transformers Can Enhance Neural Machine Translation (2021.eacl-main)
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| Challenge: | Large-scale transformers have been shown to improve neural machine translation performance but training these wider and deeper networks could be extremely memory intensive. |
| Approach: | They propose a multi-split based reversible transformer and a backpropagation algorithm that does not need to store activations for most layers. |
| Outcome: | The proposed model outperforms the vanilla transformer by at least 1.4 BLEU points in three datasets. |
Efficient Neural Machine Translation for Low-Resource Languages via Exploiting Related Languages (2020.acl-srw)
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| Challenge: | Neural Machine Translation (NMT) is a rapidly advancing MT paradigm that can be used to improve machine translation for many languages. |
| Approach: | They propose a technique called Unified Transliteration and Subword Segmentation to leverage language similarity while exploiting parallel data from related languages. |
| Outcome: | The proposed approach improves translation accuracy by 5 BLEU points over the standard Transformer-based NMT models. |
Sample, Translate, Recombine: Leveraging Audio Alignments for Data Augmentation in End-to-end Speech Translation (2022.acl-short)
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| Challenge: | End-to-end speech translation relies on data that pair source-language speech inputs with corresponding translations. |
| Approach: | They propose a method that augments transcriptions by sampling from suffix memory and translating them into target languages. |
| Outcome: | The proposed method delivers up to 0.9 and 1.1 BLEU points on top of augmentation with knowledge distillation on languages on CoVoST 2 and Europarl-ST. |
Overcoming the Rare Word Problem for low-resource language pairs in Neural Machine Translation (D19-52)
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| Challenge: | Despite above approaches can improve the prediction of rare words, they still have challenges which have adverse effects on its effectiveness. |
| Approach: | They propose three ways to address rare-word problem in neural machine translation systems . they propose an algorithm to learn morphology of unknown words for English in supervised way to minimize adverse effect of rare- word problem. |
| Outcome: | The proposed approaches improve accuracy on two low-resource language pairs. |
DOCmT5: Document-Level Pretraining of Multilingual Language Models (2022.findings-naacl)
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| Challenge: | DOCmT5 is a multilingual sequence-to-sequence language model pretraining with large-scale parallel documents. |
| Approach: | They propose a multilingual sequence-to-sequence language model pretrained with large-scale parallel documents. |
| Outcome: | The proposed model improves on baselines on document-level generation tasks. |
From Research to Production and Back: Ludicrously Fast Neural Machine Translation (D19-56)
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Young Jin Kim, Marcin Junczys-Dowmunt, Hany Hassan, Alham Fikri Aji, Kenneth Heafield, Roman Grundkiewicz, Nikolay Bogoychev
| Challenge: | Using the dominating submissions to the previous edition of the shared task, we develop improved teacher-student training via multi-agent dual-learning and noisy backward-forward translation for Transformer-based student models. |
| Approach: | They propose to use multi-agent dual-learning and noisy backward-forward translation to improve teacher-student training for Transformer-based student models. |
| Outcome: | The proposed model outperforms submissions to the previous edition of the WNGT efficiency shared task by 4 BLEU points and 10 BLUE points respectively. |
Checkpoint Reranking: An Approach to Select Better Hypothesis for Neural Machine Translation Systems (2020.acl-srw)
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| Challenge: | Neural Machine Translation (NMT) has produced excellent results in the field of machine translation due to generation of high-quality translations for different language pairs. |
| Approach: | They propose a method of re-ranking the outputs of Neural Machine Translation systems by focusing on the decoder's ability to generate distinct tokens and without the use of any language model or data. |
| Outcome: | The proposed method achieves translation improvement up to +0.16 BLEU points over baseline. |
Multiscale Collaborative Deep Models for Neural Machine Translation (2020.acl-main)
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| Challenge: | Neural machine translation models with deeper neural networks are difficult to train. |
| Approach: | They propose a MultiScale Collaborative framework to boost gradient back-propagation . they let each encoder block learn a fine-grained representation and enhance it . |
| Outcome: | The proposed framework outperforms baseline models on translation tasks with three translation directions and achieves a BLEU score of 30.56 on the English-to-German task. |
ReWE: Regressing Word Embeddings for Regularization of Neural Machine Translation Systems (N19-1)
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| Challenge: | Existing methods to regularize neural machine translation are limited in low-resource settings. |
| Approach: | They propose a method that uses regressing word embeddings to regularize neural machine translation. |
| Outcome: | The proposed system improves on a strong baseline and a state-of-the-art system. |
Domain Specific Sub-network for Multi-Domain Neural Machine Translation (2022.aacl-short)
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| Challenge: | Neural machine translation (NMT) is based on transformer models that are trained on general data from a single language pair or multiple languages. |
| Approach: | They propose a method to make masks unique per domain to improve generalization to unseen domains. |
| Outcome: | The proposed method outperforms continue training on multi-domain data on German to English translation by 1.47 BLEU points and on new domains by 1.52 BLUE points. |
Non-Autoregressive Neural Machine Translation with Consistency Regularization Optimized Variational Framework (2022.naacl-main)
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| Challenge: | Variational Autoencoder (VAE) is an effective framework to model the interdependency for non-autoregressive neural machine translation (NAT). |
| Approach: | They propose to use Variational Autoencoder to model interdependency for non-autoregressive neural machine translation (NAT) a posterior consistency regularization approach is proposed to improve translation quality . |
| Outcome: | The proposed model is 1.5/0.7 and 0.8/0.3 BLEU points faster than the baseline model. |
Multilingual Denoising Pre-training for Neural Machine Translation (2020.tacl-1)
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Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer
| Challenge: | Existing approaches to pre-train models focus on only English corpora, but this is not common in machine translation. |
| Approach: | They propose a sequence-to-sequence denoising auto-encoder pre-trained on monolingual corpora . they show that it produces significant performance gains across MT tasks . |
| Outcome: | The proposed model can achieve significant performance gains across a wide variety of MT tasks. |
Transforming Code Understanding: Clustering-Based Retrieval for Improved Summarization in Domain-Specific Languages (2025.coling-industry)
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Baban Gain, Dibyanayan Bandyopadhyay, Samrat Mukherjee, Aryan Sahoo, Saswati Dana, Palanivel Kodeswaran, Sayandeep Sen, Asif Ekbal, Dinesh Garg
| Challenge: | Existing natural language summaries of domain-specific languages are limited due to their recency and complexity. |
| Approach: | They propose a clustering-based technique to retrieve in-context examples that are semantically closer to the test example and propose eBPF prompt generation technique that yields superior-quality code summary generation. |
| Outcome: | The proposed method improves the eBPF code summarization accuracy by 12.9 BLEU points over other prompting techniques. |
Compositional Representation of Morphologically-Rich Input for Neural Machine Translation (P18-2)
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| Challenge: | Neural machine translation models are typically trained with fixed-size input and output vocabularies, which creates a bottleneck on their accuracy and generalization capability. |
| Approach: | They propose to replace the source-language embedding layer of NMT with a bi-directional recurrent neural network that generates compositional representations of the input at any desired level of granularity. |
| Outcome: | The proposed approach outperforms existing methods in a low-resource setting with five languages . the proposed approach consistently outperformed existing methods with a single word representation . |
PAIR: Planning and Iterative Refinement in Pre-trained Transformers for Long Text Generation (2020.emnlp-main)
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| Challenge: | We present a content-controlled text generation framework for pre-trained Transformers . large pre-train models are the cornerstone of many state-of-the-art models in natural language understanding and generation tasks. |
| Approach: | They propose a content-controlled text generation framework that adds content planning to large pre-trained Transformers without modifying model architecture. |
| Outcome: | The proposed framework improves the quality of the outputs on three domains. |
PARADISE: Exploiting Parallel Data for Multilingual Sequence-to-Sequence Pretraining (2022.naacl-main)
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| Challenge: | Existing methods for multilingual sequence-to-sequence pretraining rely on monolingual corpora and do not use strong cross-lingual signal contained in parallel data. |
| Approach: | They propose a method that replaces monolingual words with a bilingual dictionary and predicts the reference translation according to a parallel corpus instead of recovering the original sequence. |
| Outcome: | The proposed method improves machine translation and cross-lingual natural language inference by 2.0 BLEU points and 6.7 accuracy points over existing methods at a fraction of their computational cost. |
Large-scale Machine Translation for Indian Languages in E-commerce under Low Resource Constraints (2022.emnlp-industry)
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| Challenge: | We have deployed reliable and precise large-scale machine translation systems for several Indian regional languages. |
| Approach: | They develop a structured model development pipeline as a closed feedback loop with external manual feedback through an Active Learning component. |
| Outcome: | The proposed model improves over iterations for English to Hindi and for other languages. |
Neural Data-to-Text Generation with LM-based Text Augmentation (2021.eacl-main)
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| Challenge: | Neural data-to-text generation is a difficult task for many new applications because of a lack of training data. |
| Approach: | They propose a few-shot approach that augments the data available for training by generating new text samples based on replacing specific values by alternative ones from the same category and pairing the new text with data samples. |
| Outcome: | The proposed approach outperforms fully supervised sequence-to-sequence models with less than 10% of the training set on both datasets. |
On the Sparsity of Neural Machine Translation Models (2020.emnlp-main)
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| Challenge: | Modern neural machine translation models employ a large number of parameters, which leads to serious over-parameterization. |
| Approach: | They propose to prune parameters to improve the model by +0.8 BLEU points and to reallocate them to enhance the ability of modeling low-level lexical information. |
| Outcome: | The pruned parameters improve the model by +0.8 BLEU points and the rejuvenated parameters enhance the ability to model low-level lexical information. |
Multilingual Machine Translation: Closing the Gap between Shared and Language-specific Encoder-Decoders (2021.eacl-main)
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| Challenge: | State-of-the-art multilingual machine translation relies on a universal encoder-decoder, which requires retraining the entire system to add new languages. |
| Approach: | They propose an encoder-decoder approach that can be extended to new languages by learning their corresponding modules. |
| Outcome: | The proposed approach outperforms the universal encoder-decoder by 3.28 BLEU points on average while allowing to add new languages without retraining the rest of the modules. |
Learning Hidden Unit Contribution for Adapting Neural Machine Translation Models (N18-2)
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| Challenge: | In this paper we explore the use of Learning Hidden Unit Contribution for neural machine translation. |
| Approach: | They propose to use Learning Hidden Unit Contribution for the task of neural machine translation. |
| Outcome: | The proposed method achieves improvements of up to 2.6 BLEU points over a general system . it also achieves up to 6 BLUE points if the initial system has been trained on out-of-domain data . |
Learning To Split and Rephrase From Wikipedia Edit History (D18-1)
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| Challenge: | Performing split and rephrase tasks is one of the main operations in text simplification, alongside paraphrasing and dropping less salient content. |
| Approach: | They propose to use Wikipedia's edit history to extract a rich new dataset for the task. |
| Outcome: | The proposed model scores 32 BLEU points above the previous best on the WebSplit benchmark. |
When and Why Are Pre-Trained Word Embeddings Useful for Neural Machine Translation? (N18-2)
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| Challenge: | Pre-trained word embeddings have proven to be invaluable for improving performance in natural language analysis tasks where large-scale parallel corpora cannot be obtained. |
| Approach: | They perform five sets of experiments to analyze when pre-trained word embeddings can be useful in NMT tasks. |
| Outcome: | The embeddings provide gains of up to 20 BLEU points in the most favorable setting. |
AfroMT: Pretraining Strategies and Reproducible Benchmarks for Translation of 8 African Languages (2021.emnlp-main)
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| Challenge: | Existing reproducible benchmarks for machine translation are limited to high-resource or well-represented languages. |
| Approach: | They propose to use AfroMT to develop a reproducible machine translation benchmark for eight widely spoken African languages and a suite of analysis tools to take into account their unique properties. |
| Outcome: | The proposed benchmarks show significant improvements when pretraining on 11 languages, with gains of up to 2 BLEU points over strong baselines. |
Improving Zero-Shot Translation by Disentangling Positional Information (2021.acl-long)
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| Challenge: | Multilingual neural machine translation has shown the capability of directly translating between language pairs unseen in training, i.e. zero-shot translation. |
| Approach: | They propose to remove residual connections in an encoder layer to reduce the difficulty of generalizing to new translation directions. |
| Outcome: | The proposed model outperforms pivot-based translation in terms of quality and ease of integration of new languages. |
BitextEdit: Automatic Bitext Editing for Improved Low-Resource Machine Translation (2022.findings-naacl)
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| Challenge: | Existing methods to improve Neural Machine Translation (NMT) for lowresource languages are often trained on heuristically aligned or automatically mined data. |
| Approach: | They propose to filter out imperfect translations that yield unreliable training signals for Neural Machine Translation (NMT) instead, they propose to refine mined bitexts by automatic editing . |
| Outcome: | The proposed method improves the quality of mined bitexts for low-resource languages by up to 8 BLEU points. |
Exploring the Impact of Layer Normalization for Zero-shot Neural Machine Translation (2023.acl-short)
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| Challenge: | Recent studies have shown that layer normalization (LayerNorm) overfits training data and therefore has low generalizability for ZST. |
| Approach: | They propose to use the Transformer architecture to set the default layer normalization setting for zero-shot translation (ZST) they also propose to set LayerNorm after residual connections to outperform PreNorm by 12.3 BLEU points. |
| Outcome: | The proposed model outperforms the current model by 12.3 BLEU points on 54 directions on OPUS, IWSLT, and Europarl datasets. |
Quality Estimation-Assisted Automatic Post-Editing (2023.findings-emnlp)
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| Challenge: | Existing APE and QE combination strategies have not shown significant performance gains in the field of automatic post-editing (APE). |
| Approach: | They propose to train a model on APE and QE tasks to improve the APE performance by using a multi-task learning methodology that treats both tasks as a 'bargaining game' they also investigate various existing combination strategies and show that their approach achieves state-of-the-art performance for a ‘distant’ language pair, viz., English-Marathi. |
| Outcome: | The proposed model improves on two different language pairs, viz., English-Marathi and English-German. |
Extract and Edit: An Alternative to Back-Translation for Unsupervised Neural Machine Translation (N19-1)
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| Challenge: | Back-translation has been used in previous approaches for unsupervised neural machine translation, but pseudo sentences are of low quality as translation errors accumulate during training. |
| Approach: | They propose an approach to extract and edit real sentences from monolingual corpora and introduce a comparative translation loss to evaluate the translated target sentences. |
| Outcome: | The proposed approach outperforms state-of-the-art translation systems across two benchmarks and two low-resource language pairs by more than 2 BLEU points. |
Guiding Neural Machine Translation with Retrieved Translation Pieces (N18-1)
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| Challenge: | Neural machine translation (NMT) has trouble with lowfrequency words or phrases and generalizing across domains. |
| Approach: | They propose a method for recalling low-frequency words and phrases into neural machine translation by retrieving n-grams from a search engine and incorporating them into the decoding process. |
| Outcome: | The proposed method improves translation results up to 6 BLEU points on three narrow domain translation tasks where repetitiveness of the target sentences is particularly salient. |
Improved Zero-shot Neural Machine Translation via Ignoring Spurious Correlations (P19-1)
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| Challenge: | Existing approaches to train a multilingual NMT model for low-resource languages are lacking in terms of number of supervised examples. |
| Approach: | They propose to use decoder pre-training and back-translation to solve the degeneracy problem by analyzing spurious correlations between source and decoded sentences. |
| Outcome: | The proposed methods show significant improvement over the pivot-based approach on three challenging multilingual datasets. |
Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets (N18-1)
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| Challenge: | Experimental results show that the proposed model consistently outperforms the traditional RNNSearch and the newly emerged state-of-the-art Transformer on English-German and Chinese-English translation tasks. |
| Approach: | They propose an approach for applying GANs to NMT by building a conditional sequence generative adversarial net with two adversarials. |
| Outcome: | The proposed model outperforms the existing RNNSearch and Transformer on English-German and Chinese-English translation tasks. |
Exploiting Pre-Ordering for Neural Machine Translation (L18-1)
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| Challenge: | Existing studies have shown that Neural Machine Translation suffers from the problems that some source words are mistakenly translated for multiple times . |
| Approach: | They propose a pre-ordering approach to solve the under-translation problem by pre-ordnanced source sentences and position embedding to enhance monotone translation. |
| Outcome: | The proposed method significantly improves translation quality by 2.43 BLEU points on Chinese-to-English translation. |
A Tulu Resource for Machine Translation (2024.lrec-main)
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| Challenge: | Using parallel datasets, we train a machine translation system in English–Tulu . |
| Approach: | They present a parallel dataset for English–Tulu translation using human translations into the multilingual machine translation resource FLORES-200. |
| Outcome: | The proposed model outperforms Google Translate by 19 BLEU points (in September 2023). |
Glancing Transformer for Non-Autoregressive Neural Machine Translation (2021.acl-long)
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| Challenge: | Existing non-autoregressive neural machine translation methods are either inferior to Transformer or require multiple decoding passes, leading to reduced speedup. |
| Approach: | They propose a Glancing Language Model (GLM) for single-pass parallel generation models and Glancing Transformer (GLAT) with only single- pass decoding, GLAT is able to generate high-quality translation with 8-15 speedup. |
| Outcome: | The proposed model outperforms all previous non-autoregressive methods on multiple language directions and is nearly comparable to Transformer. |
Weight Distillation: Transferring the Knowledge in Neural Network Parameters (2021.acl-long)
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| Challenge: | Knowledge distillation is an effective method for model acceleration and compression. |
| Approach: | They propose to use parameters to distill knowledge from large neural networks to small ones . they propose to do this by using a parameter generator to transfer the knowledge to a small neural network . |
| Outcome: | The proposed method learns a small network 1.88 2.94x faster than the large network but with competitive BLEU points. |
Redistributing Low-Frequency Words: Making the Most of Monolingual Data in Non-Autoregressive Translation (2022.acl-long)
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| Challenge: | Knowledge distillation (KD) is the preliminary step for training non-autoregressive translation models, but it can lose important information for translating low-frequency words. |
| Approach: | They propose a knowledge distillation method which trains NAT student on external monolingual data with AT teacher trained on the original bilingual data. |
| Outcome: | Extensive experiments on eight WMT benchmarks show that monolingual KD outperforms the standard KD by improving low-frequency word translation without introducing any computational cost. |
Improving User Controlled Table-To-Text Generation Robustness (2023.findings-eacl)
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| Challenge: | In experiments, models perform well on test sets coming from the same distribution as the train data but their performance drops when evaluated on realistic noisy user inputs. |
| Approach: | They propose a user controlled table-to-text generation task where users explore the content in a table by selecting cells and reading a natural language description thereof. |
| Outcome: | The proposed model gains 4.85 BLEU points on user noisy test cases and 1.4 on clean test cases. |
Non-Autoregressive Neural Machine Translation: A Call for Clarity (2022.emnlp-main)
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| Challenge: | Non-autoregressive translation models require a single forward pass to generate the output sequence instead of iteratively producing each predicted token. |
| Approach: | They propose to use a single forward pass to generate the output sequence instead of iteratively producing each predicted token. |
| Outcome: | The proposed models improve translation quality and speed under third-party testing environments. |
Reusing a Pretrained Language Model on Languages with Limited Corpora for Unsupervised NMT (2020.emnlp-main)
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| Challenge: | Neural machine translation (NMT) models with limited data are ineffective when the two languages are not available for one language. |
| Approach: | They propose an approach that reuses a language model that is pretrained on two languages with large monolingual data to initialize an unsupervised neural machine translation system. |
| Outcome: | The proposed method outperforms a competitive cross-lingual pretraining model in English-Macedonian (En-Mk) and English-Albanian (En Sq) it yields more than +8.3 BLEU points for all four translation directions. |
WACO: Word-Aligned Contrastive Learning for Speech Translation (2023.acl-long)
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| Challenge: | Existing ST methods perform poorly when only a limited amount of parallel data are available for training. |
| Approach: | They propose a Word-Aligned COntrastive learning method for low-resource speech-to-text translation that bridges word-level representations for both speech and text modalities via contrastive learning. |
| Outcome: | The proposed method outperforms the best baseline by 9+ BLEU points with only 1-hour parallel ST data. |
Incremental Beam Manipulation for Natural Language Generation (2021.eacl-main)
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| Challenge: | a larger beam size can lead to deteriorating performance of natural language generation systems due to model errors . performance of NLG systems can plateau or even decrease when beam sizes larger than 10 are used . |
| Approach: | They propose to rerank the output of beam search to produce a good set of hypotheses . they propose incremental beam manipulation to discarded hypothese . |
| Outcome: | The proposed method outperforms a strong reranker on the E2E and WebNLG datasets while being on par with the existing method. |
Enhancing Language Generation with Effective Checkpoints of Pre-trained Language Model (2021.findings-acl)
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| Challenge: | Existing methods to exploit PrLMs for NLG tasks do not get as much performance gain as in the NLU task. |
| Approach: | They propose a method to integrate public checkpoints of PrLMs for the most convenience. |
| Outcome: | The proposed method significantly improves the quality of the language generation tasks on 6 different kinds of PrLMs. |
Effective Self-Mining of In-Context Examples for Unsupervised Machine Translation with LLMs (2025.findings-naacl)
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| Challenge: | Large Language Models (LLMs) have demonstrated impressive performance on a wide range of natural language processing tasks. |
| Approach: | They propose an unsupervised approach to mine in-context examples for machine translation (MT) they use word-level mining to acquire word translations that are then used to perform sentence-level mines . |
| Outcome: | The proposed approach outperforms state-of-the-art methods on 288 directions on 287 languages and is based on word-level mining and sentence-level extraction. |
Simultaneous Translation Policies: From Fixed to Adaptive (2020.acl-main)
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| Challenge: | Adaptive policies can balance translation quality and latency based on context information . previous methods on obtaining adaptive policies rely on complicated training process . |
| Approach: | They propose to obtain adaptive policies by a simple heuristic composition of fixed policies . they propose to use a heurism to obtain policies that can outperform fixed ones . |
| Outcome: | Experiments on Chinese -> English and German -> english show that adaptive policies outperform fixed policies by up to 4 BLEU points for the same latency. |
Multi-layer Representation Fusion for Neural Machine Translation (C18-1)
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| Challenge: | Neural machine translation systems require a number of stacked layers for deep models, but the prediction depends on the sentence representation of the top-most layer with no access to low-level representations. |
| Approach: | They propose a multi-layer representation fusion approach to fusing stacked layers to learn a better representation from the stack. |
| Outcome: | The proposed approach yields 0.92 and 0.56 BLEU points over the strong Transformer baseline on IWSLT German-English and NIST Chinese-English MT tasks respectively. |
Adaptive Weighting for Neural Machine Translation (C18-1)
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| Challenge: | Existing weighted sum models (WSMs) take inputs and generate one output, but they are independent of each other and are fixed for all inputs. |
| Approach: | They propose adaptive weighting for WSMs to control the contribution of each input and output state. |
| Outcome: | The proposed weighting improves translation accuracy by 1.49 and 0.92 BLEU points on Chinese-to-English translation and English-to German translation tasks. |
Grouping Words with Semantic Diversity (2021.naacl-main)
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| Challenge: | a new approach to grouping input words based on their semantic diversity is proposed . high-dimensional inputs and learning complexity hinders deep learning generalization, authors say . |
| Approach: | They propose a way to group input words based on their semantic diversity to simplify input language representation with low ambiguity. |
| Outcome: | The proposed methods generalize NLP models and demonstrate improvements on medium-scale machine translation tasks. |
Rejuvenating Low-Frequency Words: Making the Most of Parallel Data in Non-Autoregressive Translation (2021.acl-long)
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| Challenge: | Knowledge distillation (KD) is commonly used to construct synthetic data for training non-autoregressive translation models. |
| Approach: | They propose to use knowledge distillation to generate training data for non-autoregressive translation models by leveraging pretraining. |
| Outcome: | The proposed approach achieves 28.2 and 33.9 BLEU points on the WMT14 English-German and WMT16 Romanian-English datasets. |
Facilitating Terminology Translation with Target Lemma Annotations (2021.eacl-main)
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| Challenge: | Recent work on terminology integration assumes that the correct morphological forms are apriori known. |
| Approach: | They propose to train machine translation systems using a source-side data augmentation method that annotates randomly selected source language words with their target language lemmas. |
| Outcome: | The proposed method improves terminology translation accuracy in Latvian and Baltic languages. |
Multilingual Mix: Example Interpolation Improves Multilingual Neural Machine Translation (2022.acl-long)
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| Challenge: | Existing approaches to train multilingual models to learn the inductive bias of a shared vocabulary and set of parameters across languages. |
| Approach: | They propose to use a multilingual crossover encoder-decoder to fuse language pairs at an instance level to encourage sharing of input and output spaces. |
| Outcome: | The proposed approach improves quality on English-to-Many, Many-to English and zero-shot translation tasks from +0.5 BLEU up to +5.5 BLUE points. |
Anticipating Future with Large Language Model for Simultaneous Machine Translation (2025.naacl-long)
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Siqi Ouyang, Oleksii Hrinchuk, Zhehuai Chen, Vitaly Lavrukhin, Jagadeesh Balam, Lei Li, Boris Ginsburg
| Challenge: | Existing methods only use the partial utterance that has already arrived at the input and the generated hypothesis. |
| Approach: | They propose to use a large language model to predict future source words and opportunistically translate without introducing too much risk. |
| Outcome: | The proposed method outperforms baselines on four language directions and achieves the best translation quality-latency trade-off by up to 5 BLEU points at the same latency. |
Multilingual Unsupervised NMT using Shared Encoder and Language-Specific Decoders (P19-1)
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| Challenge: | Existing approaches to train multiple languages with a shared encoder and multiple decoders are based on denoising autoencoding of each language and back-translating between English and multiple non-English languages. |
| Approach: | They propose a multilingual unsupervised NMT scheme which trains multiple languages with a shared encoder and multiple decoders. |
| Outcome: | The proposed model performs better than the separately trained bilingual models on monolingual corpora and improves by 1.48 BLEU points on WMT test sets. |
Optimizing Transformer for Low-Resource Neural Machine Translation (2020.coling-main)
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| Challenge: | Language pairs with limited amounts of parallel data remain a challenge for neural machine translation. |
| Approach: | They propose to optimize a Transformer model for low-resource conditions to improve translation quality by 7.3 BLEU points compared to the default settings. |
| Outcome: | The proposed model improves translation quality up to 7.3 BLEU points compared to the default settings on the IWSLT14 training data compared with the Transformer model. |
Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings (P19-1)
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| Challenge: | Traditional parallel corpus mining methods focus on the textual content instead of the size and quality of training data. |
| Approach: | They propose a method for machine translation based on multilingual sentence embeddings. |
| Outcome: | The proposed method outperforms the best published methods on the BUCC mining task and the UN reconstruction task by more than 10 F1 and 30 precision points. |
Encoding Gated Translation Memory into Neural Machine Translation (D18-1)
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| Challenge: | Neural machine translation (MT) technology has made significant progress in the past few years. |
| Approach: | They propose a method to combine the strengths of TM and neural machine translation (NMT) they use a gating mechanism to balance the impact of the TM match on the NMT decoder . |
| Outcome: | The proposed method improves translation quality by over 10 BLEU points when fuzzy matches are higher than 50% on the UN corpus. |
Continued Pre-training on Sentence Analogies for Translation with Small Data (2024.lrec-main)
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| Challenge: | Using 10 times fewer instances, CPoA can achieve gains of +1.4 and +1.3 BLEU points over the original model. |
| Approach: | They propose to train models with analogical abilities on sentence analogies retrieved from corpus . they use a weighting scalar to adjust the influence of closer analogies while diminishing impact of far ones . |
| Outcome: | The proposed approach improves translation performance on a low-resource translation task in german-upper sorbian . it uses 10 times fewer instances to achieve gains of +1.4 and +1.3 BLEU points over the original model . |
COMET-QE and Active Learning for Low-Resource Machine Translation (2022.findings-emnlp)
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| Challenge: | Using COMET-QE, we select sentences for low-resource neural machine translation. |
| Approach: | They propose a reference-free evaluation metric to select sentences for low-resource neural machine translation using Swahili, Kinyarwanda and Spanish. |
| Outcome: | The proposed method outperforms two variants of Round Trip Translation Likelihood and random sentence selection by up to 5 BLEU points on a 30k baseline. |
SemFace: Pre-training Encoder and Decoder with a Semantic Interface for Neural Machine Translation (2021.acl-long)
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| Challenge: | Using pre-training methods for NMT models is difficult because of the cross-attention module . cross-linguistic embeddings are not used to pretrain a decoder . |
| Approach: | They propose a semantic interface between pre-trained encoder and pre-train decoder to improve NMT performance. |
| Outcome: | The proposed method improves on translation and unsupervised translation tasks. |
Energy-Based Reranking: Improving Neural Machine Translation Using Energy-Based Models (2021.acl-long)
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Sumanta Bhattacharyya, Amirmohammad Rooshenas, Subhajit Naskar, Simeng Sun, Mohit Iyyer, Andrew McCallum
| Challenge: | Autoregressive neural machine translation (NMT) uses a tractable likelihood computation and efficient sampling. |
| Approach: | They propose to use an energy-based model to mimic the behavior of the task measure and use it to train an energy based re-ranking algorithm. |
| Outcome: | The proposed model improves on the samples drawn from the NMT with a higher BLEU score than the experimental model and the energy-based re-ranking algorithm. |
Improving Machine Translation with Phrase Pair Injection and Corpus Filtering (2022.emnlp-main)
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| Challenge: | In this paper, we show that the combination of Phrase Pair Injection and Corpus Filtering boosts the performance of Neural Machine Translation systems. |
| Approach: | They propose to combine Phrase Pair Injection and Corpus Filtering to boost performance of Neural Machine Translation systems. |
| Outcome: | The proposed method improves machine translation models on low-resource language pairs . BLEU score improves over models trained with whole pseudo-parallel corpus augmented with parallel corpus. |
Alternative Input Signals Ease Transfer in Multilingual Machine Translation (2022.acl-long)
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| Challenge: | Recent work in multilingual machine translation (MMT) has focused on the potential of positive transfer between languages. |
| Approach: | They propose to augment training data with alternative signals that unify different writing systems, such as phonetic, romanized, and transliterated input. |
| Outcome: | The proposed model outperforms strong ensemble baselines on Indic and Turkic languages by 1.3 BLEU points on both languages. |
Improving Low-Resource NMT through Relevance Based Linguistic Features Incorporation (2020.coling-main)
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| Challenge: | Existing studies on incorporating arbitrary syntactic information into neural machine translation (NMT) are lacking. |
| Approach: | They propose to integrate linguistic knowledge at different levels into neural machine translation framework to improve translation quality for language pairs with extremely limited data. |
| Outcome: | The proposed methods improve translation quality for all tasks by 3.09 BLEU points . the proposed methods are based on two different approaches . |
Penalizing Divergence: Multi-Parallel Translation for Low-Resource Languages of North America (2022.coling-1)
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| Challenge: | Existing studies show that multi-parallel translation models can overfit when training data are limited. |
| Approach: | They introduce a regularizer which penalizes translation models when they represent source sentences with identical target translations in divergent ways. |
| Outcome: | The proposed model improves when the target data for all language pairs are identical. |
Vocabulary Adaptation for Domain Adaptation in Neural Machine Translation (2020.findings-emnlp)
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| Challenge: | Neural network methods exhibit strong performance only in a few resource-rich domains. |
| Approach: | They propose a method that fine-tunes embedding layers of a pre-trained NMT model to the target domain. |
| Outcome: | The proposed method improves fine-tuning performance in En-Ja and De-En translation by 3.86 and 3.28 BLEU points. |
The Two Shades of Dubbing in Neural Machine Translation (2020.coling-main)
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| Challenge: | Dubbing has two shades; synchronisation constraints are applied only when the actor’s mouth is visible on screen, while the translation is unconstrained for off-screen dubbing. |
| Approach: | They annotate an existing dubbing corpus for this dichotomy and find that on-screen dubbing is more difficult for MT than off-screen. |
| Outcome: | The results show that on-screen dubbing is more difficult for MT than off-screen translation, and that synchronisation constraints dramatically decrease translation quality for off- screen dubbing. |
Byte-based Multilingual NMT for Endangered Languages (2022.coling-1)
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| Challenge: | Existing work has not studied how byte encoding can benefit endangered languages . multilingual neural machine translation (MNMT) models suffer from out-of-vocabulary issues and representation bottleneck . |
| Approach: | They propose a multilingual multilingual neural machine translation system to alleviate the representation bottleneck and improve translation performance in endangered languages. |
| Outcome: | The proposed system outperforms subword-based models on twelve languages up to +18.5 BLEU points, an 840% relative improvement over baseline models. |
Where’s the Point? Self-Supervised Multilingual Punctuation-Agnostic Sentence Segmentation (2023.acl-long)
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| Challenge: | Prior sentence segmentation tools rely on punctuation or require a large amount of training data . a new method for multilingual sentence segmenting is proposed to replace the best prior tools by using only sentence-segmented examples. |
| Approach: | They propose a punctuation-agnostic sentence segmentation method that uses newline characters which implicitly perform segmentation into paragraphs. |
| Outcome: | The proposed method outperforms all prior best sentence segmentation tools by 6.1% F1 points. |
Unsupervised Statistical Machine Translation (D18-1)
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| Challenge: | Neural Machine Translation (NMT) systems can be trained from monolingual corpora without supervision. |
| Approach: | They propose a phrase-based approach that trains from monolingual corpora . their method is based on phrase-driven Statistical Machine Translation (SMT) they propose to train NMT systems without supervision from monolinguistic corpors . |
| Outcome: | The proposed approach improves on the existing supervised systems by combining a phrase table with an n-gram language model and fine-tuning hyperparameters through an unsupervised MERT variant. |
Reinforcement Learning with Large Action Spaces for Neural Machine Translation (2022.coling-1)
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| Challenge: | Recent work has argued that the gains produced by Reinforcement learning are mostly due to promoting tokens that have already received a fairly high probability in pre-training. |
| Approach: | They hypothesize that the large action space is a main obstacle to RL’s effectiveness in MT by reducing the size of the vocabulary without changing the vocabulary. |
| Outcome: | The proposed method improves by 1.5 BLEU points on average. |
Robust Neural Machine Translation with Doubly Adversarial Inputs (P19-1)
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| Challenge: | Neural machine translation (NMT) models suffer from noisy perturbations in the input . a gradient-based method to craft adversarial examples informed by the translation loss is proposed . |
| Approach: | They propose an approach to improve the robustness of NMT models by attacking the translation model with adversarial source examples and defending the model with a target input. |
| Outcome: | The proposed approach improves translation performance and robustness on clean inputs and higher on noisy data. |
Machine Translation With Weakly Paired Documents (D19-1)
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| Challenge: | Recent studies explore the possibility of unsupervised machine translation with monolingual data only. |
| Approach: | They propose a method to mine bilingual sentences from weakly paired documents . they use word distribution-level alignments to constrain word distributions of two weakly-paired documents. |
| Outcome: | The proposed method outperforms previous results on six translation tasks using weakly paired bilingual documents and a large number of bilingual sentences. |
Semantically Consistent Data Augmentation for Neural Machine Translation via Conditional Masked Language Model (2022.coling-1)
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| Challenge: | Neural network models have a large number of parameters to train, but data augmentation is relatively under-explored in natural language processing. |
| Approach: | They propose a bi-directional conditional Masked Language Model (CMLM) that can be conditional on both left and right contexts and the label. |
| Outcome: | The proposed method achieves the best performance on four translation datasets and yields up to 1.90 BLEU points over the baseline. |
XLM-D: Decorate Cross-lingual Pre-training Model as Non-Autoregressive Neural Machine Translation (2022.emnlp-main)
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| Challenge: | Existing pre-training language models have been successful in natural language understanding and autoregressive generation tasks, but non-autoregressive models have not been sufficiently successful. |
| Approach: | They propose a pre-trained masked language model (MLM) and a non-autoregressive generation model with a lightweight decorator. |
| Outcome: | The proposed model outperforms the previous mask-predict model on translation datasets by 19.9x. |
Multiword Expression aware Neural Machine Translation (2020.lrec-1)
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| Challenge: | Multiword Expressions (MWEs) are a pervasive phenomenon in all natural languages and challenge NLP applications because of their unpredictable morpho-syntactic and lexico--semantic behaviour. |
| Approach: | They propose to use linguistic resources to improve MWE translation and MWE generation by up to 5.09 BLEU points on MWE test sets. |
| Outcome: | The proposed annotation and data augmentation improve translation quality and increase performance by up to 5.09 BLEU points on MWE test sets. |
CQG: A Simple and Effective Controlled Generation Framework for Multi-hop Question Generation (2022.acl-long)
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| Challenge: | Current models can not ensure the complexity of generated questions, so they may generate shallow questions that can be answered without multi-hop reasoning. |
| Approach: | They propose a controlled framework to generate multi-hop questions that contain key entities in multi- hop reasoning chains and a novel Transformer-based decoder to guarantee that key entities appear in the questions. |
| Outcome: | The proposed model outperforms the state-of-the-art model 25% on HotpotQA. |
Dynamic Data Selection and Weighting for Iterative Back-Translation (2020.emnlp-main)
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| Challenge: | Experimental results demonstrate that our methods achieve improvements of up to 1.8 BLEU points over competitive baselines. |
| Approach: | They propose a data selection and weighting strategy to iterate back-translation models and apply it to it . they use a target language to back-transcribe monolingual data, which is of high quality and reflect the target domain. |
| Outcome: | The proposed approach achieves 1.8 BLEU points over baselines on domain adaptation, low-resource, and high-resourced MT settings and on two language pairs. |
Joint Speech Transcription and Translation: Pseudo-Labeling with Out-of-Distribution Data (2023.findings-acl)
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| Challenge: | a recent study shows that self-training can improve upon fully supervised baselines in low-resource settings for several sequence-to-sequence tasks. |
| Approach: | They propose to use pseudo-labeling to label unsupervised data and add it to the training pool. |
| Outcome: | The proposed setup improves on the unsupervised data by using pseudo-labeling . the proposed setup provides 0.4% absolute WER and 2.1 BLEU points for En–De . |
Revisiting Source Context in Nearest Neighbor Machine Translation (2023.emnlp-main)
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| Challenge: | Existing research does not explicitly consider the source context when retrieving similar examples . |
| Approach: | They propose a method to improve neural machine translation via source context enhancement by integrating a source-aware distance calibration module. |
| Outcome: | The proposed approach can be integrated with representative kNN-MT baselines and achieve significant performance improvements. |
CCMatrix: Mining Billions of High-Quality Parallel Sentences on the Web (2021.acl-long)
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| Challenge: | Using a curated common crawl corpus, we were able to mine 10.8 billion parallel sentences out of which only 2.9 billions are aligned with English. |
| Approach: | They use 32 snapshots of a curated common crawl corpus totaling 71 billion unique sentences to mine 10.8 billion parallel sentences out of which only 2.9 billions are aligned with English. |
| Outcome: | The proposed system outperforms the best single systems on the WMT’19 test set for English-German/Russian/Chinese and outperformed the best submission at the 2020 WAT workshop. |
A Tree-based Decoder for Neural Machine Translation (D18-1)
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| Challenge: | Existing work on adding syntactic information to NMT systems is limited to linguistically-inspired tree structures. |
| Approach: | They propose an NMT model that can naturally generate the topology of an arbitrary tree structure on the target side. |
| Outcome: | The proposed model outperforms standard seq2seq models by 2.1 BLEU points and other methods for incorporating target-side syntax by 0.7 BLUE points. |
Greedy Search with Probabilistic N-gram Matching for Neural Machine Translation (D18-1)
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| Challenge: | Neural machine translation models are usually trained with word-level loss under teacher forcing algorithm . however, this method suffers from exposure bias due to high variance of gradient estimation . |
| Approach: | They propose a method with a differentiable sequence-level training objective . they use greedy search to alleviate the problem of exposure bias . |
| Outcome: | Experiments on Chinese-to-English translation tasks show that the proposed method outperforms the reinforcement-based methods. |
AdvAug: Robust Adversarial Augmentation for Neural Machine Translation (2020.acl-main)
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| Challenge: | Recent work in neural machine translation has led to dramatic improvements in both research and commercial systems. |
| Approach: | They propose a adversarial augmentation method for Neural Machine Translation that minimizes vicinal risk over virtual sentences . they use a novel vicinity distribution for adversarials to describe a smooth interpolated embedding space . |
| Outcome: | The proposed method outperforms the current method on Chinese-English, English-French, and English-German translation benchmarks. |
DiMS: Distilling Multiple Steps of Iterative Non-Autoregressive Transformers for Machine Translation (2023.findings-acl)
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| Challenge: | Neural machine translation models follow an autoregressive decoding strategy, generating the target sentence one token at a time. |
| Approach: | They propose a distillation technique that reduces the number of decoding steps required to reach a certain translation quality. |
| Outcome: | The proposed model achieves 7.8 and 12.9 BLEU points improvements in translation accuracy on distilled and raw versions of WMT’14 De-En. |
Phrase-Based & Neural Unsupervised Machine Translation (D18-1)
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| Challenge: | Recent advances in machine translation have reported near human-level performance on several languages, yet their effectiveness strongly relies on the availability of large amounts of parallel sentences. |
| Approach: | They propose two models that leverage a careful initialization of the parameters and denoising effect of language models. |
| Outcome: | The proposed models outperform the current methods on English-French and German-English benchmarks while being simpler and having fewer hyper-parameters. |
EAG: Extract and Generate Multi-way Aligned Corpus for Complete Multi-lingual Neural Machine Translation (2022.acl-long)
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| Challenge: | Existing approaches to build multi-way aligned corpus from bilingual data are limited by their scale. |
| Approach: | They propose to build a multi-way aligned corpus from bilingual data using two steps to extract candidate alignes and generate the final alignets from the candidates. |
| Outcome: | The proposed method improves on two publicly available datasets with +1.1 and +1.4 BLEU points. |
SegAugment: Maximizing the Utility of Speech Translation Data with Segmentation-based Augmentations (2023.findings-emnlp)
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| Challenge: | End-to-end Speech Translation models are limited by a data bottleneck . end-to end models can address several shortcomings of cascaded models . |
| Approach: | They propose a data augmentation strategy to augment sentence-level datasets by using an Audio Segmentation system to re-segment the speech of each document with different length constraints. |
| Outcome: | The proposed method achieves state-of-the-art results in MuST-C and in mTEDx. |
Generalized Data Augmentation for Low-Resource Translation (P19-1)
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| Challenge: | Low-resource language pairs with a lack of parallel data pose challenges for machine translation . data augmentation using monolingual data is an effective way to alleviate the problem . |
| Approach: | They propose a general framework for data augmentation for low-resource machine translation using monolingual data and a related high-resourced language. |
| Outcome: | The proposed method improves translation quality by 1.5 to 8 BLEU points under extreme low-resource settings compared to baselines. |
Training and Adapting Multilingual NMT for Less-resourced and Morphologically Rich Languages (L18-1)
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| Challenge: | Using multilingual and multi-way neural machine translation approaches is a major advantage . training NMT systems for individual language pairs takes significantly more time than training of SMT systems . |
| Approach: | They propose to employ multilingual and multi-way neural machine translation approaches for morphologically rich languages such as Estonian and Russian. |
| Outcome: | The proposed approach improves translation quality by +3.27 BLEU points over baseline models. |
CoSTA: Code-Switched Speech Translation using Aligned Speech-Text Interleaving (2025.coling-main)
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| Challenge: | More than half of the world's population is presumed to be bilingual . spoken translation of code-switched speech has been under-explored . |
| Approach: | They propose an end-to-end model architecture CoSTA that scaffolds on pretrained ASR and MT modules. |
| Outcome: | The proposed model outperforms existing models by 3.5 BLEU points in spoken translation of code-switched speech. |
DictDis: Dictionary Constrained Disambiguation for Improved NMT (2024.findings-emnlp)
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| Challenge: | Existing approaches to domain-specific neural machine translation (NMT) are lexically constrained and draw from domain- specific dictionaries. |
| Approach: | They propose a lexically constrained neural machine translation system that disambiguates between multiple dictionary candidates. |
| Outcome: | The proposed system disambiguates between multiple candidate translations derived from dictionaries on English-Hindi, English-German, and English-French datasets. |
Shape of Synth to Come: Why We Should Use Synthetic Data for English Surface Realization (2020.acl-main)
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| Challenge: | In the Surface Realization Shared Tasks of 2018 and 2019, there was little difference in absolute performance between systems trained with and without synthetic data. |
| Approach: | They propose to use synthetic data to explore approaches to surface realization from Universal-Dependency-like trees to surface strings for several languages. |
| Outcome: | The proposed method improves performance of a previously state-of-the-art system by 8 BLEU points over the previous system on the English dataset. |
Instruction Position Matters in Sequence Generation with Large Language Models (2024.findings-acl)
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| Challenge: | Large language models (LLMs) can perform conditional sequence generation tasks, such as translation or summarization, through instruction fine-tuning. |
| Approach: | They propose to shift the position of task instructions after the input sentences to enhance the model's instruction-following capability. |
| Outcome: | The proposed method outperforms traditional settings across various model scales (1B / 7B & 13B) and different sequence generation tasks (translation and summarization) without any additional data or annotation costs. |
Extended Parallel Corpus for Amharic-English Machine Translation (2022.lrec-1)
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| Challenge: | Existing approaches to automate the complex task of translation are tedious and expensive. |
| Approach: | They describe acquisition, preprocessing, segmentation, and alignment of an Amharic-English parallel corpus. |
| Outcome: | The proposed corpus outperforms statistical machine translation models by six to seven BLEU points . the results show that the subword models outperformed word-based models by three to four BLUE points compared with the word-base models . |
CTC-based Non-autoregressive Speech Translation (2023.acl-long)
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Chen Xu, Xiaoqian Liu, Xiaowen Liu, Qingxuan Sun, Yuhao Zhang, Murun Yang, Qianqian Dong, Tom Ko, Mingxuan Wang, Tong Xiao, Anxiang Ma, Jingbo Zhu
| Challenge: | End-to-end speech translation (E2E ST) and non-autoregressive (NAR) generation are promising in language and speech processing for their advantages of less error propagation and low latency. |
| Approach: | They develop a model that uses connectionist temporal classification to predict the source and target texts. |
| Outcome: | The proposed model achieves an average BLEU score of 29.5 with a speed-up of 5.67. |
Attention as a Guide for Simultaneous Speech Translation (2023.acl-long)
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| Challenge: | EDAtt uses attention patterns to determine when to emit partial translations . results show that it yields better results compared to existing SimulST policies . |
| Approach: | They propose an adaptive policy that exploits attention patterns between audio source and target textual translation to guide an offline-trained ST model during simultaneous inference. |
| Outcome: | The proposed policy yields better results on en->de, compared to the current state of the art. |
“A Little is Enough”: Few-Shot Quality Estimation based Corpus Filtering improves Machine Translation (2023.findings-acl)
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| Challenge: | Quality Estimation (QE) is the task of evaluating the quality of a translation when reference translation is unavailable. |
| Approach: | They propose a Quality Estimation based Filtering approach to extract high-quality parallel data from the pseudo-parallel corpus. |
| Outcome: | The proposed approach improves the machine translation system performance by up to 1.8 BLEU points over the baseline model. |
Modeling Orthographic Variation Improves NLP Performance for Nigerian Pidgin (2024.lrec-main)
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| Challenge: | Existing datasets for Nigerian Pidgin are characterised by noise in the form of orthographic variations. |
| Approach: | They propose a phonetic-theoretic framework to generate orthographic variations to augment training data. |
| Outcome: | The proposed framework improves machine translation and sentiment analysis by combining real and synthesized orthographic variations. |
Neural Machine Translation between Low-Resource Languages with Synthetic Pivoting (2024.lrec-main)
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| Challenge: | Pivot-based neural machine translation systems overcome data scarcity by including a high-resource pivot language in the process of translating between low-resourced languages. |
| Approach: | They propose a novel approach to pivot-based translation in which pivot sentences are generated synthetically from both the source and target languages. |
| Outcome: | The proposed approach improves pivot-based systems translating between low-resource Southern African languages by up to 5.6 BLEU points after fine-tuning. |
Make Imagination Clearer! Stable Diffusion-based Visual Imagination for Multimodal Machine Translation (2025.acl-long)
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Andong Chen, Yuchen Song, Kehai Chen, Xuefeng Bai, Muyun Yang, Liqiang Nie, Jie Liu, Tiejun Zhao, Min Zhang
| Challenge: | Experimental results show that our model significantly outperforms existing multimodal MT and text-only MT. |
| Approach: | They propose a stable diffusion-based imagination network into a multimodal large language model to generate an image for each source sentence. |
| Outcome: | The proposed model outperforms existing multimodal and text-only MT and achieves an average improvement of 14 BLEU points on Multi30K and MSCOCO multimodal MT benchmarks. |
GrammaMT: Improving Machine Translation with Grammar-Informed In-Context Learning (2025.acl-long)
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| Challenge: | Experiments show that GrammaMT enhances translation performance on open-source instruction-tuned LLMs for various low- to high-resource languages across three benchmarks: (1) largest corpus, (2) challenging 2023 SIGMORPHON Shared Task data, (3) even in an out-of-domain setting with FLORES. |
| Approach: | They propose a grammatically-aware prompting approach that uses Interlinear Glossed Text . they propose gloss-shot, chain-gloss and model-glooss prompting strategies that are training-free . |
| Outcome: | Experiments show that GrammaMT improves translation performance on open-source instruction-tuned LLMs for various low- to high-resource languages across three benchmarks. |
The African Languages Lab: A Collaborative Approach to Advancing Low-Resource African NLP (2026.acl-long)
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Sheriff Issaka, Keyi Wang, Yinka Ajibola, Oluwatumininu Samuel-Ipaye, Zhaoyi Zhang, Nicte Aguillon Jimenez, Evans Kofi Agyei, Abraham Lin, Rohan Ramachandran, Sadick Abdul Mumin, Faith Nchifor, Mohammed Shuraim Issah, Erick Rosas Gonzalez, Lieqi Liu, Sylvester Kpei, Jemimah Kusi Osei, Carlene Ajeneza, Persis Boateng, Prisca Adwoa Dufie Yeboah, Saadia Gabriel
| Challenge: | Among the approximately 7,000 languages spoken globally, fewer than 20 receive substantial attention in NLP research. |
| Approach: | They propose to use African multi-modal speech and text data to validate African multimodal models and validate them on targeted language data. |
| Outcome: | The African Languages Lab's results show that the proposed model outperforms untrained models in 31 languages and a 1B-parameter model beats the commercial system in Yoruba and Twi. |