Papers with BLEU scores
AligNART: Non-autoregressive Neural Machine Translation by Jointly Learning to Estimate Alignment and Translate (2021.emnlp-main)
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| Challenge: | Non-autoregressive neural machine translation models suffer from the multi-modality problem . aligNART leverages full alignment information to explicitly reduce the modality of the target distribution . |
| Approach: | They propose an alignment decomposition method which explicitly reduces the modality of the target distribution. |
| Outcome: | The proposed model outperforms previous models that focus on modality reduction on two translation tasks. |
Fine-Grained and Multi-Dimensional Metrics for Document-Level Machine Translation (2025.naacl-srw)
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| Challenge: | Large language models excel in machine translation, but most studies focus on sentence-level translation. |
| Approach: | They propose to use LLMs as a judge paradigm to evaluate document-level translations by directly prompting them to translate entire documents in a single pass. |
| Outcome: | The proposed method improves translation quality even without document-level fine-tuning compared to translating sentences separately . |
Compact and Robust Models for Japanese-English Character-level Machine Translation (D19-52)
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| Challenge: | In recent years, neural machine translation (NMT) has made a great progress, and its translation quality has far surpassed the conventional statistical machine translation. |
| Approach: | They propose a character-level translation model which is mid-gated and multi-attention model for Japanese-English translation and propose to train them using a relatively narrow beam of width 4 or 5 . |
| Outcome: | The proposed models can translate the word containing Katakana by coining out a close word, and the model can produce tolerable results for noised sentences. |
Recycling a Pre-trained BERT Encoder for Neural Machine Translation (D19-56)
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| Challenge: | In monolingual tasks, the number of unlearned model parameters is as huge as the number learned parameters in the BERT model. |
| Approach: | They propose to apply a pre-trained Bidirectional Encoder Representations from Transformers (BERT) model to Transformer-based neural machine translation (NMT) based on the Transformer. |
| Outcome: | The proposed model is stable and efficient in low-resource settings. |
Binary and Ternary Natural Language Generation (2023.acl-long)
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| Challenge: | ternary and binary neural networks have proven difficult to optimize since both parameter and output space are discretized . authors demonstrate ternaries and binary models on downstream tasks of summarization and machine translation . |
| Approach: | They propose to use ternary and binary neural networks to optimize for multiplication-free computation . they propose to apply statistics-based quantization for the weights and elastic quantization of the activations to the transformer text generation model. |
| Outcome: | The proposed model outperforms the best existing models on machine translation tasks. |
A Simple and Fast Strategy for Handling Rare Words in Neural Machine Translation (2022.aacl-srw)
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| Challenge: | Neural Machine Translation (NMT) has been gaining popularity due to its ability to bias in highfrequency words, low-frequency words have little chance of being considered in the inference process. |
| Approach: | They propose a strategy for integrating constraints during the training and decoding process to improve the translation of rare words. |
| Outcome: | The proposed approach improves translation of rare words in high and low-resource translation tasks, showing improvements of up to +1.8 BLEU scores over baseline systems. |
KNU-HYUNDAI’s NMT system for Scientific Paper and Patent Tasks onWAT 2019 (D19-52)
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Cheoneum Park, Young-Jun Jung, Kihoon Kim, Geonyeong Kim, Jae-Won Jeon, Seongmin Lee, Junseok Kim, Changki Lee
| Challenge: | We submitted our transformer-based neural machine translation system to the translation tasks of the 6th workshop on Asian Translation (WAT 2019). |
| Approach: | They propose a transformer-based neural machine translation system for Chinese-Japanese, English-Japanese, and Korean->Japanoise translation tasks. |
| Outcome: | The proposed system performed well on the two translation tasks and was ranked first in terms of the BLEU scores in all the JPC2 subtasks. |
SuperNMT: Neural Machine Translation with Semantic Supersenses and Syntactic Supertags (P18-3)
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| Challenge: | Neural Machine Translation models have become the state-of-the-art in the field of machine translation. |
| Approach: | They incorporate semantic supersensetags and syntactic supertag features into EN–FR and EN–DE factored NMT systems and show that they improve model training. |
| Outcome: | The proposed model training improves on EN–FR and EN–DE factored NMT systems. |
Zero-shot North Korean to English Neural Machine Translation by Character Tokenization and Phoneme Decomposition (2020.acl-srw)
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| Challenge: | a limited number of North Korean to English translation models have been developed . a zero-shot approach is proposed to train a neural machine translation model using South Korean data . |
| Approach: | They propose a method to tokenize South Korean input sentences and decompose them into phonemes. |
| Outcome: | The proposed method improves the BLEU scores by +1.01 points compared with the baseline . the proposed method can learn North Korean to English translation and improve the linguistic accuracy. |
Japanese Predicate Conjugation for Neural Machine Translation (N18-4)
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| Challenge: | Neural machine translation (NMT) has a drawback in that it can generate only high-frequency words owing to the computational costs of the softmax function in the output layer. |
| Approach: | They propose two methods to generate low-frequency words and deal with unknown words using Japanese predicate conjugation information without discarding linguistic information. |
| Outcome: | The proposed methods can generate low-frequency words and deal with unknown words. |
Semantics of the Unwritten: The Effect of End of Paragraph and Sequence Tokens on Text Generation with GPT2 (2021.acl-srw)
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| Challenge: | Experimental results show that pre-trained language model GPT2 can generate better continuations by learning to generate the in the fine-tuning stage. |
| Approach: | They conduct experiments on an English essay dataset using Chinese-GPT2 . they find that the model can generate better continuations by learning to generate the in the fine-tuning stage. |
| Outcome: | The pre-trained language model GPT2 can generate better continuations by learning to generate the in the fine-tuning stage. |
Neural Machine Translation for Low Resource Languages using Bilingual Lexicon Induced from Comparable Corpora (N18-4)
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| Challenge: | Recent crowdsourcing efforts and workshops on machine translation have resulted in small amounts of parallel texts for building viable machine translation systems for low resource pairs. |
| Approach: | They propose to use an end-to-end Siamese bidirectional recurrent neural network to extract parallel sentences from Wikipedia to improve BLEU scores on both NMT and phrase-based SMT systems. |
| Outcome: | The proposed approach improves BLEU scores on both NMT and phrase-based SMT systems for the low-resource language pairs English–Hindi and English–Tamil when compared to training exclusively on the limited bilingual corpora. |
Unsupervised Pretraining for Neural Machine Translation Using Elastic Weight Consolidation (P19-2)
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| Challenge: | Neural machine translation (NMT) uses sequence to sequence architectures, but requires a huge amount of parallel data. |
| Approach: | They use Elastic Weight Consolidation to regularize weights of two language models . they then fine-tune the model on parallel data to avoid forgetting the original task . |
| Outcome: | The proposed method achieves BLEU scores similar to the previous work, but is slower and requires less training data. |
ViDove: A Translation Agent System with Multimodal Context and Memory-Augmented Reasoning (2025.emnlp-demos)
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Yichen Lu, Wei Dai, Jiaen Liu, Ching Wing Kwok, Zongheng Wu, Xudong Xiao, Ao Sun, Sheng Fu, Jianyuan Zhan, Yian Wang, Takatomo Saito, Sicheng Lai
| Challenge: | Recent advances in Large Language Models (LLMs) have demonstrated remarkable capabilities in Machine Translation (MT) tasks. |
| Approach: | They propose a translation agent system designed for multimodal input that leverages visual and contextual background information to enhance the translation process. |
| Outcome: | The proposed translation agent achieves significantly higher translation quality in subtitle generation and general translation tasks compared to previous state-of-the-art systems. |
NeKo: Cross-Modality Post-Recognition Error Correction with Tasks-Guided Mixture-of-Experts Language Model (2025.acl-industry)
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Yen-Ting Lin, Zhehuai Chen, Piotr Zelasko, Zhen Wan, Xuesong Yang, Zih-Ching Chen, Krishna C Puvvada, Ke Hu, Szu-Wei Fu, Jun Wei Chiu, Jagadeesh Balam, Boris Ginsburg, Yu-Chiang Frank Wang, Chao-Han Huck Yang
| Challenge: | Existing methods to train a model on a mixture of domain datasets require separate correction language models. |
| Approach: | They propose a multi-task correction MoE that trains experts to become an "expert" of speech-to-text, language-totext and vision-to text datasets by learning to route each dataset’s tokens to its mapped expert. |
| Outcome: | The proposed model outperforms GPT-3.5 and Claude-3.5-Sonnet on the Open ASR Leaderboard and reaches an average relative 5.0% WER reduction and substantial improvements in BLEU scores. |
Using Neural Machine Translation Methods for Sign Language Translation (2022.acl-srw)
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| Challenge: | Sign languages are the main medium of exchanging information for the deaf and hard of hearing. |
| Approach: | They propose to use two NMT architectures to train models on parallel German Sign Language corpora . they achieve substantial improvement in BLEU scores for the models trained on the two corporales . |
| Outcome: | The proposed models achieve significant improvements on the two corpora trained on the german sign language . the proposed models outperform the models trained on both corporales . |
Mixed Multi-Head Self-Attention for Neural Machine Translation (D19-56)
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| Challenge: | Recent advances in neural machine translation have been made in the field of multi-head self-attention and there is no explicit mechanism to ensure that different attention heads capture different features. |
| Approach: | They propose a novel multi-head self-attention model which models not only global and local attention but also forward and backward attention in different attention heads. |
| Outcome: | The proposed model improves on WAT17 English-Japanese and IWSLT14 German-English translation tasks without increasing the number of parameters. |
Auto-Sizing the Transformer Network: Improving Speed, Efficiency, and Performance for Low-Resource Machine Translation (D19-56)
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| Challenge: | Neural sequence-to-sequence models are sensitive to architecture and hyperparameter settings. |
| Approach: | They incorporate architecture search into a single training run through auto-sizing . they show that auto-size can improve BLEU scores by up to 3.9 points . |
| Outcome: | The proposed algorithm improves BLEU scores on low-resource language pairs while removing one-third of the parameters from the model. |
TransLLaMa: LLM-based Simultaneous Translation System (2024.findings-emnlp)
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| Challenge: | Decoder-only large language models have limited applications in simultaneous machine translation . naively translating each source word immediately results in compromised target quality . |
| Approach: | a study shows that a pre-trained open-source LLM can control input segmentation directly by generating a special "wait" token. |
| Outcome: | a new open-source model can control input segmentation directly by generating a special "wait" token. |
Jointly Masked Sequence-to-Sequence Model for Non-Autoregressive Neural Machine Translation (2020.acl-main)
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| Challenge: | masked language models have been used for natural language processing tasks but few studies have adopted it in the sequence-to-sequence models. |
| Approach: | They propose to combine encoder and decoder to train a masked sequence-to-sequence model . they propose to train the encoder more rigorously by masking the encoded input . |
| Outcome: | The proposed model achieves 27.69/32.24 BLEU scores on English-German/German-English tasks with 5+ times speed up compared with an autoregressive model. |
Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs (2020.tacl-1)
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| Challenge: | Recent graph-to-text models generate text from graph data using global or local aggregation . global node encoding allows explicit communication between two distant nodes, but fails to capture long-range relationships. |
| Approach: | They propose to combine global and local aggregation to learn node representations . they propose to use global and locally encoding to learn contextualized node embeddings based on graph data . |
| Outcome: | The proposed models outperform state-of-the-art models on two graph-to-text datasets by 18.01 and 63.69 points. |
A Large Automatically-Acquired All-Words List of Multiword Expressions Scored for Compositionality (L18-1)
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| Challenge: | Existing literature on semantically idiosyncratic multiword expressions is limited to English . idiomatic expressions are phraseological units consisting of more than one lexeme and exhibit some kind of idiom. |
| Approach: | They propose to make available a large automatically-acquired all-words list of English multiword expressions scored for compositionality. |
| Outcome: | The proposed list improves the BLEU scores of the English multiword expressions. |
Fixed Encoder Self-Attention Patterns in Transformer-Based Machine Translation (2020.findings-emnlp)
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| Challenge: | Recent studies have shown that attention heads learn simple positional patterns . |
| Approach: | They propose to replace all but one attention head of each encoder layer with simple fixed – non-learnable – attentive patterns that are solely based on position and do not require external knowledge. |
| Outcome: | The proposed model improves translation quality and improves BLEU scores by up to 3 points in low-resource scenarios. |
Improving Neural Machine Translation by Incorporating Hierarchical Subword Features (C18-1)
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| Challenge: | Using subwords, we find that the appropriate subword units for the three layers differ depending on the model . incorporating hierarchical subword features improves BLEU scores on the IWSLT evaluation datasets. |
| Approach: | They propose a method that expresses a word by combining "subwords" they propose to incorporate hierarchical subword features into a single embedding layer . |
| Outcome: | The proposed method improves BLEU scores on the IWSLT evaluation datasets. |
One-step Nonautoregressive Natural Language Generation with Shortcut Flow Matching Models (2026.acl-short)
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| Challenge: | Recent diffusion-based approaches to text generation are inefficient due to the need for multiple denoising steps. |
| Approach: | They propose a shortcut flow-matching model that learns to directly predict multi-step denoising outcomes in a single step. |
| Outcome: | The proposed model improves on three datasets and can predict multi-step denoising outcomes in a single step. |
Using Semantic Similarity as Reward for Reinforcement Learning in Sentence Generation (P19-2)
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| Challenge: | Existing models for sentence generation use cross-entropy loss as the loss function . however, cross-etropy is unable to evaluate sentences as a whole and lacks flexibility . et al., 2018: a novel approach to improve sentence generation models . |
| Approach: | They propose a method to train a model using estimated semantic similarity between output and reference sentences to alleviate cross-entropy loss problems. |
| Outcome: | The proposed model improves the BLEU scores from the baseline LSTM NMT model. |
Domain Adaptation of Machine Translation with Crowdworkers (2022.emnlp-industry)
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| Challenge: | Existing methods for machine translation work well when no in-domain data are available. |
| Approach: | They propose a framework that efficiently and effectively collects parallel sentences in a target domain from the web with the help of crowdworkers. |
| Outcome: | The proposed method improves the BLEU scores by an average of +7.8 points compared to a general-purpose translation model. |
Sub-Word Alignment is Still Useful: A Vest-Pocket Method for Enhancing Low-Resource Machine Translation (2022.acl-short)
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| Challenge: | Low-resource machine translation (MT) is challenging due to the scarcity of parallel data and lack of bilingual dictionaries. |
| Approach: | They propose to leverage embedding duplication between aligned sub-words to extend the Parent-Child transfer learning method to improve low-resource machine translation. |
| Outcome: | The proposed method achieves BLEU scores of 22.5, 28.0 and 18.1 respectively. |
Iterative Refinement in the Continuous Space for Non-Autoregressive Neural Machine Translation (2020.emnlp-main)
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| Challenge: | Existing non-autoregressive inference procedures that refine in token space often require computational overhead. |
| Approach: | They propose an efficient inference procedure that iteratively refines translation purely in the continuous space using a latent variable instead of the latent variables. |
| Outcome: | The proposed procedure is twice as efficient and more effective than the existing EM-like inference procedure. |
Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks (N18-2)
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| Challenge: | Semantic representations have long been argued as potentially useful for enforcing meaning preservation and improving generalization performance of machine translation methods. |
| Approach: | They propose to integrate semantic representations into neural machine translation by injecting a semantic bias into sentence encoders and achieving improvements in BLEU scores. |
| Outcome: | The proposed representations achieve better BLEU scores over the linguistic-agnostic and syntax-aware versions on the English–German language pair. |
Unstructured Minds, Predictable Machines: A Comparative Study of Narrative Cohesion in Human and LLM Stream-of-Consciousness Writing (2025.acl-srw)
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| Challenge: | Stream-of-consciousness narratives are a challenge for large language models (LLMs) authors examined differences between human and LLM-generated narratives to assess narrative coherence and personality expression. |
| Approach: | They generate SoC narratives by prompting LLMs with the first half of SoC-essays while either providing the models with the personality characteristics (Big Five) or omitting them. |
| Outcome: | The proposed models showed low similarity between LLM-generated continuations and original human texts, as measured by cosine similarity, perplexity, and BLEU scores. |
Translating Hanja Historical Documents to Contemporary Korean and English (2022.findings-emnlp)
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| Challenge: | The Annals of Joseon Dynasty contain the daily records of the Kings of Joseont, the 500-year kingdom preceding the modern nation of Korea. |
| Approach: | They propose a neural machine translation model that translates historical documents written in Hanja to more easily understandable Korean and to English. |
| Outcome: | The proposed model outperforms baseline models in terms of BLEU scores for both contemporary Korean and English translations. |
Rapid Adaptation of Neural Machine Translation to New Languages (D18-1)
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| Challenge: | Existing approaches to adapt neural machine translation systems to low-resource languages are difficult to implement and require large amounts of training data. |
| Approach: | They propose a method to train neural machine translation systems to new low-resource languages . they propose to start with massively multilingual "seed models" and continue training on data related to the LRL . |
| Outcome: | The proposed method achieves BLEU scores of up to 15.5 with no data from the LRL and improves over other adaptation methods by 1.7 BLUE points average over 4 LRL settings. |
WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia (2021.eacl-main)
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| Challenge: | a new approach to extract parallel sentences from Wikipedia articles is proposed . the approach is based on multilingual sentence embeddings, but does not limit it to English . |
| Approach: | They propose to automatically extract parallel sentences from Wikipedia articles in 96 languages . they train neural MT baseline systems on the mined data and evaluate them on the TED corpus . |
| Outcome: | The proposed approach extracts parallel sentences from Wikipedia articles in 96 languages . the extracted sentences achieve strong BLEU scores for many language pairs . |
Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation (N18-1)
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| Challenge: | Existing approaches to neural machine translation have computational complexities that are either linear or exponential in the number of constraints. |
| Approach: | They propose an algorithm for lexically constrained decoding with a complexity of O(1) in the number of constraints. |
| Outcome: | The proposed algorithm can place constraints and improve results in simulated post-editing tasks. |
Syntactically Supervised Transformers for Faster Neural Machine Translation (P19-1)
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| Challenge: | Standard decoders for neural machine translation generate a single token per timestep, which slows inference . a series of controlled experiments demonstrates that SynST decodes sentences 5x faster than the baseline autoregressive Transformer. |
| Approach: | They propose a syntactically supervised Transformer that generates all target tokens in one shot . synST is a variant of the Transformer architecture that autoregressively predicts a chunked parse tree . |
| Outcome: | The proposed method decodes sentences 5x faster than the baseline method on En-De and En-Fr datasets while achieving higher BLEU scores. |
“Wikily” Supervised Neural Translation Tailored to Cross-Lingual Tasks (2021.emnlp-main)
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| Challenge: | Unsupervised neural machine translation models perform well in low-resource or distant languages. |
| Approach: | They propose a model that leverages Wikipedia for machine translation and cross-lingual tasks without supervision from external parallel data or supervised models in target language. |
| Outcome: | The proposed model outperforms supervised models in Arabic and English translation tasks. |
Simpler and Faster Learning of Adaptive Policies for Simultaneous Translation (D19-1)
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| Challenge: | Recent work on simultaneous translation is difficult because of its latency and quality. |
| Approach: | They propose a supervised-learning framework to learn adaptive policies from parallel text sequences . they use a model that predicts when a target word is read or WRITE if context provides enough information . |
| Outcome: | Experiments on German=>English show that the proposed method can learn flexible policies with better BLEU scores and similar latencies compared to previous work. |
STT4SG-350: A Speech Corpus for All Swiss German Dialect Regions (2023.acl-short)
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Michel Plüss, Jan Deriu, Yanick Schraner, Claudio Paonessa, Julia Hartmann, Larissa Schmidt, Christian Scheller, Manuela Hürlimann, Tanja Samardžić, Manfred Vogel, Mark Cieliebak
| Challenge: | We present a corpus of Swiss German speech annotated with Standard German text at the sentence level. |
| Approach: | They present a corpus of Swiss German speech annotated with Standard German sentences . they use a web app to show the speakers standard German sentences and record them . |
| Outcome: | The corpus contains 343 hours of speech from all Swiss German dialect regions . it is the largest public speech corpus for Swiss German to date . |
CoCoa: An Encoder-Decoder Model for Controllable Code-switched Generation (2022.emnlp-main)
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| Challenge: | Generating code-switched text with fine-grained control on the degree of code-witching and the lexical choices used to convey formality has been well-explored. |
| Approach: | They propose to generate code-switched text with fine-grained control on the degree of code-changing and lexical choices used to convey formality. |
| Outcome: | The proposed model can be invoked at test-time to synthesize code-switched text faithful to syntactic and lexical attributes relevant to code-witching. |
Alternated Training with Synthetic and Authentic Data for Neural Machine Translation (2021.findings-acl)
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| Challenge: | Existing approaches to synthesizing data in NMT focus on leveraging monolingual data in training. |
| Approach: | They propose alternated training with synthetic and authentic data to improve NMT models' performance. |
| Outcome: | The proposed approach improves Chinese-English and German-English translation tasks over strong baselines. |
Dynamic and Efficient Inference for Text Generation via BERT Family (2023.acl-long)
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| Challenge: | Existing methods to improve performance of pre-trained language models are limited due to large-scale parameters and the universal autoregressive decoding paradigm. |
| Approach: | They propose a novel fine-tuning method which can make a single pre-trained model support Dynamic and Efficient infERence and achieve an adaptive trade-off between model performance and latency. |
| Outcome: | The proposed method achieves higher BLEU scores than the strong autoregressive Transformer model on translation tasks with 3 12 times speedup and faster inference speed compared with the BART model on four GLGE benchmark tasks. |
Pronoun-Targeted Fine-tuning for NMT with Hybrid Losses (2020.emnlp-main)
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| Challenge: | Popular machine translation model training uses backtranslation to improve BLEU scores . we use generative-discriminative hybrid losses to fine-tune a trained model . |
| Approach: | They propose a class of conditional generative-discriminative hybrid losses to fine-tune a machine translation model. |
| Outcome: | The proposed model improves on a sentence-level and contextual model without additional data. |
Self-Supervised Neural Machine Translation (P19-1)
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| Challenge: | Neural machine translation (NMT) methods relied on the availability of high-quality parallel corpora. |
| Approach: | They propose a method where an emergent NMT system is used for selecting training data and learning internal NMT representations. |
| Outcome: | The proposed method achieves BLEU scores of 29.21 (en2fr) and 27.36 (fr2en) on newstest2014 using English and French Wikipedia data for training. |
Stacked Acoustic-and-Textual Encoding: Integrating the Pre-trained Models into Speech Translation Encoders (2021.acl-long)
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| Challenge: | End-to-end Speech Translation (E2E ST) encoders lack global context representation, whereas MT encoder lacks it. |
| Approach: | They propose a Stacked Acoustic-and-Textual Encoding method for speech translation . they propose an adaptor module to alleviate representation inconsistency . |
| Outcome: | The proposed method achieves state-of-the-art BLEU scores of 18.3 and 25.2 on two ST tasks. |
Confidence Based Bidirectional Global Context Aware Training Framework for Neural Machine Translation (2022.acl-long)
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| Challenge: | Existing studies focus on how to effectively exploit bidirectional global contexts in neural machine translation models. |
| Approach: | They propose a Confidence Based Bidirectional Global Context Aware training framework for NMT . they incorporate bidirectional global context to the NMT model on unconfidently-predicted target words . |
| Outcome: | The proposed framework improves the NMT model on three large-scale translation datasets by +1.02, +0.57 BLEU scores. |
Subword Mapping and Anchoring across Languages (2021.findings-emnlp)
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| Challenge: | State-of-the-art multilingual systems rely on shared vocabularies that cover all considered languages. |
| Approach: | They propose a method to construct bilingual subword vocabularies by mapping and anchoring subwords together over multiple languages. |
| Outcome: | The proposed method improves zero-shot transfer to an unseen language without task-specific data, but only by sharing subword embeddings. |
Unsupervised Neural Machine Translation for Low-Resource Domains via Meta-Learning (2021.acl-long)
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| Challenge: | Unsupervised machine translation suffers from data-scarce domains, authors report . a meta-learning algorithm trains the model to adapt to another domain by utilizing only a small amount of training data. |
| Approach: | They propose a meta-learning algorithm that trains the model to adapt to another domain . their model surpasses a transfer learning-based approach by up to 2-3 BLEU scores . |
| Outcome: | The proposed algorithm outperforms a transfer learning-based approach by 2-3 BLEU scores . the proposed model outperformed previous models in the domain of unsupervised machine translation . |
Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generation (N19-1)
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| Challenge: | Modern neural generation systems conflate these two steps into a single end-to-end differentiable system. |
| Approach: | They propose to split the generation process into a symbolic text-planning stage that is faithful to the input, followed by a neural generation stage that focuses only on realization. |
| Outcome: | The proposed method improves reliability and adequacy while maintaining fluent output. |
Modelling Context Emotions using Multi-task Learning for Emotion Controlled Dialog Generation (2021.eacl-main)
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| Challenge: | Recent research has tackled this task using neural generative methods by augmenting emotion classes with the input sequences. |
| Approach: | They propose to use a self-attention based encoder and a decoder with dot product attention mechanism to generate a viable response with a specified emotion. |
| Outcome: | The proposed model outperforms baselines on automatic evaluation measures such as F1 and BLEU scores, thus resulting in more fluent and adequate responses. |
Encouraging Lexical Translation Consistency for Document-Level Neural Machine Translation (2021.emnlp-main)
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| Challenge: | Experimental results show document-level neural machine translation improves lexical consistency . inconsistent translations tend to confuse readers in some cases . |
| Approach: | They propose to use a word link to obtain a document word link and an auxiliary loss function to constrain that their translation should be consistent. |
| Outcome: | The proposed approach improves translation consistency on ChineseEnglish and EnglishFrench translation tasks. |
Ensemble of MRR and NDCG models for Visual Dialog (2021.naacl-main)
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| Challenge: | BLEU scores favor correct syntax over semantics. |
| Approach: | They propose a non-parametric ranking method that integrates the ranks of two strong MRR and NDCG models into a single ranking that excels on both metrics. |
| Outcome: | The proposed model can keep the MRR and NDCG models state-of-the-art and the NDGC models state of the art. |
Parallel Corpora for bi-lingual English-Ethiopian Languages Statistical Machine Translation (C18-1)
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Solomon Teferra Abate, Michael Melese, Martha Yifiru Tachbelie, Million Meshesha, Solomon Atinafu, Wondwossen Mulugeta, Yaregal Assabie, Hafte Abera, Binyam Ephrem, Tewodros Abebe, Wondimagegnhue Tsegaye, Amanuel Lemma, Tsegaye Andargie, Seifedin Shifaw
| Challenge: | Various approaches to machine translation have been and are being used in the research community, that can broadly classified as rule-based and corpus based. |
| Approach: | They propose to develop parallel corpora for English and Ethiopian languages such as Amharic, Tigrigna, Afan-Oromo, Wolaytta and Ge’ez. |
| Outcome: | The proposed system improves on the English-Ethiopian languages. |
G-Transformer for Document-Level Machine Translation (2021.acl-long)
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| Challenge: | Existing work extends translation unit from single sentence to multiple sentences. |
| Approach: | They propose to introduce locality assumption as an inductive bias into Transformer and reduce the hypothesis space of attention from target to source. |
| Outcome: | The proposed model achieves state-of-the-art BLEU scores on three benchmark datasets. |
Neural Caption Generation for News Images (L18-1)
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| Challenge: | Existing methods for automatic caption generation of images are lacking in the field of image-related applications. |
| Approach: | They propose a method for automatically generating captions for news images . they propose several deep neural network architectures built upon Recurrent Neural Networks . |
| Outcome: | The proposed method outperforms a traditional method on a BBC News dataset using automatic evaluation and human evaluation. |
Chart-to-Text: A Large-Scale Benchmark for Chart Summarization (2022.acl-long)
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Shankar Kantharaj, Rixie Tiffany Leong, Xiang Lin, Ahmed Masry, Megh Thakkar, Enamul Hoque, Shafiq Joty
| Challenge: | Inferring key insights from charts can be challenging and time-consuming. |
| Approach: | They propose a task where the goal is to explain a chart and summarize key takeaways from it in natural language. |
| Outcome: | The proposed model produces fluent summaries but suffers from hallucinations and factual errors . the proposed model is compared with other models and can be used to generate BLEU scores . |
A Multilingual View of Unsupervised Machine Translation (2020.findings-emnlp)
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| Challenge: | Empirically, we show that our approach results in higher BLEU scores over state-of-the-art unsupervised models on the WMT’14 English-French, WMT'16 English-German, and WMT‘16 English–Romanian datasets in most directions. |
| Approach: | They propose a probabilistic framework for multilingual neural machine translation that encompasses supervised and unsupervised setups, focusing on unsupervised translation. |
| Outcome: | The proposed framework achieves higher BLEU scores than state-of-the-art unsupervised models on the WMT’14 English-French, WMT'16 English-German, and WMT‘16 English–Romanian datasets in most directions. |
Bi-SimCut: A Simple Strategy for Boosting Neural Machine Translation (2022.naacl-main)
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| Challenge: | BLEU scores of 31.16 for ende and 38.37 for deen on the IWSLT14 dataset, 30.78 for entde, 35.15 for de en and 27.17 for zhen . |
| Approach: | They propose a bidirectional pretraining and unidirectional finetuning procedure to boost NMT performance. |
| Outcome: | The proposed method achieves strong translation performance across five datasets. |
Learning and Analyzing Generation Order for Undirected Sequence Models (2021.findings-emnlp)
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| Challenge: | Undirected neural sequence models generate monotonically from left to right in machine translation tasks. |
| Approach: | They train a policy that learns the generation order for a pre-trained, undirected translation model via reinforcement learning. |
| Outcome: | The proposed policy outperforms heuristic generation orders on three out of four language pairs. |
A Retrieve-and-Rewrite Initialization Method for Unsupervised Machine Translation (2020.acl-main)
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| Challenge: | Recent work shows successful methods for unsupervised machine translation (UMT) initialization stage is important since bad initialization may wrongly squeeze the search space and too much noise may hurt the final performance. |
| Approach: | They propose a retrieval and rewriting based method to better initialize unsupervised translation models. |
| Outcome: | The proposed method improves translation performance by over 4 BLEU scores. |
Scene Graph as Pivoting: Inference-time Image-free Unsupervised Multimodal Machine Translation with Visual Scene Hallucination (2023.acl-long)
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| Challenge: | Current neural machine translation (NMT) relies on parallel sentences, which obstructs the development of NMT for minor languages. |
| Approach: | They propose an unsupervised multimodal machine translation setup where the model is trained with source-text image pairs and tested with only source- text inputs. |
| Outcome: | The proposed model outperforms the baseline model on the task and setup, helping yield translations with better completeness, relevance and fluency without relying on paired images. |
Using Semantic Role Labeling to Improve Neural Machine Translation (2022.lrec-1)
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| Challenge: | despite progress in machine translation, some form of language understanding may be desirable . current systems rely on pattern recognition, but some form may be useful . |
| Approach: | They use semantic role labeling to annotate a standard parallel corpus with semantic roles . they then train a neural machine translation system using the annotated corpus and original unannotated text . |
| Outcome: | The proposed system improves BLEU scores for English, French, German, Greek and Spanish. |
SURF: Semantic-level Unsupervised Reward Function for Machine Translation (2022.naacl-main)
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| Challenge: | Reinforcement Learning (RL) is dependent on the reward formulation due to the intrinsic difficulty of the task in the high-dimensional discrete action space and the sparseness of the standard reward functions. |
| Approach: | They propose a maximally dense semantic-level unsupervised reward function which mimics human evaluation by considering both sentence fluency and semantic similarity. |
| Outcome: | The proposed reward outperforms the standard sparse reward by 2% on average for in- and out-of-domain settings. |
Neural Stylistic Response Generation with Disentangled Latent Variables (2021.acl-long)
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| Challenge: | Existing parallel datasets for creating stylistic responses are not stylistically consistent. |
| Approach: | They propose to disentangle the content and style in latent space by diluting sentence-level information in style representations. |
| Outcome: | The proposed approach achieves a higher BERT-based style intensity score and comparable BLEU scores, compared with baselines. |
SimulSpeech: End-to-End Simultaneous Speech to Text Translation (2020.acl-main)
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| Challenge: | SimulSpeech is an end-to-end simultaneous speech to text translation system . conventional approaches to simultaneous speech translation divide the translation process into two stages . |
| Approach: | They develop an end-to-end simultaneous speech to text translation system which translates speech in source language to text in target language concurrently. |
| Outcome: | The proposed system achieves reasonable BLEU scores and lower delay compared to full-sentence translation model. |
Reducing Disambiguation Biases in NMT by Leveraging Explicit Word Sense Information (2022.naacl-main)
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| Challenge: | Recent studies show that Neural Machine Translation models struggle to disambiguate polysemous words without lapsing into their most frequent senses. |
| Approach: | They propose a way to automatically create high-precision sense-annotated parallel corpora . they then propose 'fine-tuning' strategies to exploit these sense annotations during training . |
| Outcome: | The proposed approach achieves higher BLEU scores than its vanilla counterpart in 3 language pairs. |
Multi-Level Memory for Task Oriented Dialogs (N19-1)
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| Challenge: | Recent task oriented dialog systems use memory architectures to incorporate external knowledge in their dialogs. |
| Approach: | They propose a novel multi-level memory architecture that separates dialog context and knowledge base results . they use cells for each query and their corresponding results to address queries . |
| Outcome: | The proposed architecture outperforms current state-of-the-art models on three publicly available data sets. |
Unifying Input and Output Smoothing in Neural Machine Translation (2020.coling-main)
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| Challenge: | Recent methods that smooth input and output of neural machine translation systems bring significant improvements in performance. |
| Approach: | They propose a method that replaces one-hot representations with soft posterior distributions of an external language model, smoothing the input of machine translation systems. |
| Outcome: | The proposed method improves translation performance on small datasets and larger datasets. |
F-MALLOC: Feed-forward Memory Allocation for Continual Learning in Neural Machine Translation (2024.naacl-long)
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| Challenge: | Existing approaches to address Catastrophic Forgetting (CF) have been developed to avoid forgetting and maintain system extensibility. |
| Approach: | They propose a method to reduce Catastrophic Forgetting (CF) by decomposing feed-forward layers into discrete memory cells and ensuring robust extendability. |
| Outcome: | The proposed method achieves higher BLEU scores and almost zero forgetting while maintaining robust extendability. |
Stochastic Wasserstein Autoencoder for Probabilistic Sentence Generation (N19-1)
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| Challenge: | Experimental results show that the latent space learned by WAE exhibits properties of continuity and smoothness as in VAEs. |
| Approach: | They propose to use the variational autoencoder (VAE) for probabilistic sentence generation . they propose a variant of WAE that encourages the stochasticity of the encoder . |
| Outcome: | The proposed variant encourages the stochasticity of the encoder while achieving higher BLEU scores. |
SpanAlign: Sentence Alignment Method based on Cross-Language Span Prediction and ILP (2020.coling-main)
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| Challenge: | Existing methods for automatic sentence alignment assume monotonic alignments, but they can handle non-monotonic alignments. |
| Approach: | They propose a method to automatically extract parallel sentences from noisy parallel documents by embeddings and encoding each source and target sentence. |
| Outcome: | The proposed method improves translation accuracy by 4.1 BLEU scores on English-Japanese . it can predict spans in target document from sentences in source document . |
LITERA: An LLM Based Approach to Latin-to-English Translation (2025.findings-naacl)
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| Challenge: | Recent advances in large language models (LLMs) have shown promise in addressing these challenges across languages like Latin. |
| Approach: | They propose a Latin-to-English translation platform based on GPT-4o and GPT4o that combines a fine-tuned version of GPT-3o and a sophisticated algorithm to produce literal translations. |
| Outcome: | The model is based on two languages: Latin Interpretation and Translations into English for Research Assistance and GPT-4o. |
Speech Sense Disambiguation: Tackling Homophone Ambiguity in End-to-End Speech Translation (2024.acl-long)
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| Challenge: | End-to-end speech translation (ST) models require simultaneous crossmodal and crosslingual transformations to be effective. |
| Approach: | They propose a homophone-aware contrastive learning approach that integrates a speech-text masking strategy to reduce ambiguity. |
| Outcome: | The proposed approach achieves SOTA results on BLEU scores on different MuST-C and CoVoST ST tasks, underlining its effectiveness in reducing speech sense ambiguity. |
Towards Understanding and Improving Knowledge Distillation for Neural Machine Translation (2023.acl-long)
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| Challenge: | Existing knowledge distillation techniques for neural machine translation lack special treatment on the top-1 information, which is limiting the potential of KD. |
| Approach: | They propose a method to distill knowledge from top-1 predictions of teachers and a technique to infuse more additional knowledge by distilling on the data without ground-truth targets. |
| Outcome: | The proposed method outperforms the vanilla word-level KD and outperfies the existing methods on three different students with different capacity gaps. |
Dialectal and Low Resource Machine Translation for Aromanian (2025.coling-main)
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| Challenge: | Existing training methods for low-resource languages are focused on English or are massively multilingual, but do not consider the particularities of lowresource language. |
| Approach: | They propose a neural machine translation system that can translate between Romanian, English, and Aromanian. |
| Outcome: | The proposed system can translate between Romanian, English, and Aromanian . BLEU scores range from 17 to 32 depending on direction and genre of text . |
Modeling Context With Linear Attention for Scalable Document-Level Translation (2022.findings-emnlp)
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| Challenge: | Document-level machine translation models lack quadratic complexity in the sequence length due to their attention layers. |
| Approach: | They evaluate a recent linear attention model with a sentential gate to promote a recency inductive bias and compare it to open-source document translation. |
| Outcome: | The proposed model significantly improves translation quality on IWSLT 2015 and OpenSubtitles 2018 with similar or better BLEU scores. |
How Do Seq2Seq Models Perform on End-to-End Data-to-Text Generation? (2022.acl-long)
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| Challenge: | Existing models for data-to-text generation are based on pipelines and end-to end architectures. |
| Approach: | They use multidimensional quality metrics to evaluate models on end-to-end data-totext generation and compare their performance against pipeline models. |
| Outcome: | The proposed model improves in Omission and Inaccuracy Extrinsic errors but increases errors such as Addition. |
BERT, mBERT, or BiBERT? A Study on Contextualized Embeddings for Neural Machine Translation (2021.emnlp-main)
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| Challenge: | Existing methods for incorporating pre-trained models into NMT systems are non-trivial and lack a comparison of the impact that other pre-trainers may have on translation performance. |
| Approach: | They propose to use the input of a bilingual pre-trained language model as the input for NMT encoders and a stochastic layer selection approach to ensure sufficient utilization of contextualized embeddings. |
| Outcome: | The proposed bilingual pre-trained language model outperforms all other pre-train models on the IWSLT’14 dataset and the proposed dual-directional translation model. |
A Generative Framework for Simultaneous Machine Translation (2021.emnlp-main)
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| Challenge: | Existing approaches use a fixed number of source words to translate or learn dynamic policies for the number of sources by reinforcement learning. |
| Approach: | They propose a generative framework that uses a latent variable to model read or translate actions at every time step and integrates out to consider all possible translation policies. |
| Outcome: | The proposed framework achieves the best BLEU scores on benchmark datasets. |
LCS: A Language Converter Strategy for Zero-Shot Neural Machine Translation (2024.findings-acl)
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| Challenge: | Existing LT strategies cannot indicate the desired target language on zero-shot translation, i.e., the off-target issue. |
| Approach: | They propose a language converter strategy that embeds the target language into the top encoder layers to mitigate confusion in the encoder and ensures stable language indication for the decoder. |
| Outcome: | The proposed language converter strategy significantly mitigates off-target issue on multiUN, TED, and OPUS-100 datasets. |
Modeling Graph Structure in Transformer for Better AMR-to-Text Generation (D19-1)
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| Challenge: | Recent studies on AMR-to-text generation formalize the task as a sequence-tosequence learning problem . previous approaches only consider the relations between directly connected concepts while ignoring the rich structure in AMR graphs. |
| Approach: | They propose a structure-aware self-attention approach to model the relations between indirectly connected concepts in the seq2seq model. |
| Outcome: | The proposed approach outperforms the state-of-the-art on English AMR benchmarks . it significantly outperformed the state of the art on the benchmarks, with 29.66 and 31.82 BLEU scores . |
Esposito: An English-Persian Scientific Parallel Corpus for Machine Translation (2024.lrec-main)
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| Challenge: | Existing scientific corpus for English-Persian language pairs is lacking . supervised neural machine translation requires millions of parallel sentences . |
| Approach: | They propose a parallel corpus called Esposito which contains 3.5 million parallel sentences . they also propose 'test sets' that might serve as a baseline for future studies . |
| Outcome: | The proposed system improves the baseline on average by 7.6 and 8.4 BLEU scores for English-Persian language pairs. |
CLAD-ST: Contrastive Learning with Adversarial Data for Robust Speech Translation (2023.emnlp-main)
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| Challenge: | Cascaded approach is the most popular choice for speech translation, but lacks robustness when dealing with noisy inputs. |
| Approach: | They propose a cascaded approach that uses an automatic speech recognition model and a machine translation model to translate speech in one language to text in another language. |
| Outcome: | The proposed approach achieves significant gains of up to 3 BLEU scores in English-German and English-French speech translation without hurting the translation quality on clean text. |
Domain-Aware k-Nearest-Neighbor Knowledge Distillation for Machine Translation (2024.findings-acl)
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| Challenge: | Existing methods to transfer knowledge from kNN datastore into new models are expensive and arbitrarily transfer knowledge. |
| Approach: | They propose a domain-aware method which filters out domain-relevant neighborhood knowledge for learning in the distillation process. |
| Outcome: | The proposed method achieves state-of-the-art on four domain translation tasks. |
Scientific Credibility of Machine Translation Research: A Meta-Evaluation of 769 Papers (2021.acl-long)
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| Challenge: | a meta-evaluation of machine translation (MT) has been conducted in 769 research papers . a recent study shows that evaluation practices have changed over the past decade . |
| Approach: | They propose a meta-evaluation method for machine translation that uses BLEU scores to evaluate MT performance. |
| Outcome: | The proposed meta-evaluation of machine translation shows that evaluation practices have changed over the past decade . the authors suggest that the evaluation process should be streamlined and standardized to ensure the validity of the evaluation method . |
ODE Transformer: An Ordinary Differential Equation-Inspired Model for Sequence Generation (2022.acl-long)
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Bei Li, Quan Du, Tao Zhou, Yi Jing, Shuhan Zhou, Xin Zeng, Tong Xiao, JingBo Zhu, Xuebo Liu, Min Zhang
| Challenge: | Residual networks are an Euler discretization of solutions to Ordinary Differential Equations (ODE). |
| Approach: | They propose a residual block of layers in Transformer that can be described as a higher-order solution to ODE. |
| Outcome: | The proposed architecture can gain large improvements over strong baselines at a slight cost in inference efficiency. |
Learning Kernel-Smoothed Machine Translation with Retrieved Examples (2021.emnlp-main)
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| Challenge: | Existing methods to update deployed models are prone to overfit . however, non-parametric methods are liable to over-fit the retrieved examples . |
| Approach: | They propose to learn Kernel-Smoothed Translation with Example Retrieval (KSTER) this approach allows users to adapt models to emerging cases without retraining . |
| Outcome: | The proposed approach achieves 1.1 to 1.5 BLEU scores over existing methods without retraining . the proposed model is released on https://github.com/jiangqn/KSTER. |
Simultaneous Translation with Flexible Policy via Restricted Imitation Learning (P19-1)
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| Challenge: | Existing approaches to simultaneous translation have been limited and use fixed-latency policies or a complicated two-staged model. |
| Approach: | They propose a single model that adds a “delay” token to the target vocabulary and a restricted dynamic oracle to greatly simplify training. |
| Outcome: | The proposed model achieves better BLEU scores and lower latencies compared to fixed and RL-learned policies on Chinese -> English simultaneous translation. |
TransFace: Unit-Based Audio-Visual Speech Synthesizer for Talking Head Translation (2024.findings-acl)
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Xize Cheng, Rongjie Huang, Linjun Li, Zehan Wang, Tao Jin, Aoxiong Yin, Chen Feiyang, Xinyu Duan, Baoxing Huai, Zhou Zhao
| Challenge: | Existing methods for talking head translation rely on cascading, resulting in delays and cascadic errors. |
| Approach: | They propose a model for talking head translation, TransFace, which can translate audio-visual speech into audio-visual speech in other languages. |
| Outcome: | The proposed model can translate audio-visual speech into audio-visual speech in other languages. |
Set to Ordered Text: Generating Discharge Instructions from Medical Billing Codes (D19-1)
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| Challenge: | a neural architecture learns to generate content in a specific order without explicit specifications of the relations between input entities and output entities. |
| Approach: | They propose a natural language generation task that generates discharge instructions from ICD codes . they propose to model content ordering and text generation in a specific order . |
| Outcome: | The proposed model outperforms baseline models in BLEU scores and human evaluation. |
Hard-Coded Gaussian Attention for Neural Machine Translation (2020.acl-main)
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| Challenge: | Recent work has questioned the importance of multi-headed attention in achieving high translation quality. |
| Approach: | They develop a “hard-coded” attention variant without any learned parameters. |
| Outcome: | The proposed model reduces BLEU scores by adding a single learned cross attention head to an otherwise hard-coded Transformer. |
The Learnability of the Annotated Input in NMT Replicating (Vanmassenhove and Way, 2018) with OpenNMT (2020.lrec-1)
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| Challenge: | reproducibility of experiments is a key issue in Neural Networks, which are fed with variable samples of training data. |
| Approach: | They reproduce some of the experiments related to neural network training for Machine Translation as reported in . they annotated a sample from the EN-FR and EN-DE Europarl with syntactic and semantic annotations to train neural networks with the Nematus Neural Machine Translation toolkit. |
| Outcome: | The results obtained were lower than the original paper, but on a more limited set of annotations. |
Translationese as a Language in “Multilingual” NMT (2020.acl-main)
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| Challenge: | Recent work examines the impact of translationese in machine translation evaluation using the WMT evaluation campaign. |
| Approach: | They propose to use a sentence-level classifier to distinguish translationese from original target text to generate a machine translation model that can produce more natural outputs at test time. |
| Outcome: | The proposed model produces more natural outputs at test time, yielding gains in human evaluation scores on accuracy and fluency. |
GuoFeng: A Benchmark for Zero Pronoun Recovery and Translation (2022.emnlp-main)
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Mingzhou Xu, Longyue Wang, Derek F. Wong, Hongye Liu, Linfeng Song, Lidia S. Chao, Shuming Shi, Zhaopeng Tu
| Challenge: | ZPs are often omitted when they can be pragmatically or grammatically inferred from intraand inter-sentential contexts. |
| Approach: | They propose a benchmark testset for target evaluation on Chinese-English ZP translation. |
| Outcome: | The proposed testset covers five genres and identifies current challenges for evaluation. |
Lexical Translation Inconsistency-Aware Document-Level Translation Repair (2023.findings-acl)
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| Challenge: | Experimental results show document-level translation repair improves translation consistency but still suffers from lexical translation inconsistency due to the lack of inter-sentence context. |
| Approach: | They propose a document-level translation repair model to model translation inconsistency via automatic post-editing. |
| Outcome: | The proposed model improves translation quality and lexical consistency on document-level translation datasets. |
DISCO: A Large Scale Human Annotated Corpus for Disfluency Correction in Indo-European Languages (2023.findings-emnlp)
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| Challenge: | Existing research on disfluency correction has primarily focused on English due to the unavailability of large-scale open-source datasets. |
| Approach: | They propose to use an annotated human-annotated corpus to analyze disfluency correction in four important Indo-European languages to demonstrate the benefits. |
| Outcome: | The proposed model improves BLEU scores by 5.65 points when used with a state-of-the-art machine translation system. |
Finding the Optimal Byte-Pair Encoding Merge Operations for Neural Machine Translation in a Low-Resource Setting (2024.findings-emnlp)
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| Challenge: | Using different byte pair encoder configurations, we can improve neural machine translation performance for low-resource languages. |
| Approach: | They investigate the impact of different Byte Pair Encoding configurations on neural machine translation performance for the Filipino-Cebuano language pair across various text domains. |
| Outcome: | The proposed methods show that smaller BPE configurations yield higher BLEU scores, indicating improved translation quality through finer tokenization granularity . larger BPE setups and the absence of BPE result in lower BLUE scores, suggesting a decline in translation quality due to coarser tokenisation. |
Few-Shot Data-to-Text Generation via Unified Representation and Multi-Source Learning (2023.acl-long)
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Alexander Hanbo Li, Mingyue Shang, Evangelia Spiliopoulou, Jie Ma, Patrick Ng, Zhiguo Wang, Bonan Min, William Yang Wang, Kathleen McKeown, Vittorio Castelli, Dan Roth, Bing Xiang
| Challenge: | Existing methods for data-to-text generation focus on specific types of structured data. |
| Approach: | They propose a method that provides a unified representation that can handle various forms of structured data such as tables, knowledge graph triples, and meaning representations. |
| Outcome: | The proposed method improves zero-shot and few-shot scenarios and can adapt to new structured data. |
M-Ped: Multi-Prompt Ensemble Decoding for Large Language Models (2025.findings-emnlp)
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Jiaxin Guo, Daimeng Wei, Yuanchang Luo, Hengchao Shang, Zongyao Li, Jinlong Yang, Zhanglin Wu, Zhiqiang Rao, Shimin Tao, Hao Yang
| Challenge: | a new ensemble decoding approach enhances the performance of Large Language Models. |
| Approach: | They propose a multi-prompt ensemble decoding approach to enhance LLM performance . they submit n variations of prompts with X to LLMs in batch mode to decode and derive probability distributions . |
| Outcome: | The proposed method improves pass@k rates, LENS metrics and BLEU scores on diverse NLP tasks. |
Languages Still Left Behind: Toward a Better Multilingual Machine Translation Benchmark (2025.emnlp-main)
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Chihiro Taguchi, Seng Mai, Keita Kurabe, Yusuke Sakai, Georgina Agyei, Soudabeh Eslami, David Chiang
| Challenge: | Multilingual machine translation (MT) benchmarks are widely used to evaluate the capabilities of modern MT systems. |
| Approach: | They propose to use a multilingual machine translation benchmark to assess the capabilities of modern machine translation systems. |
| Outcome: | The FLORES+ benchmark claims to maintain a translation quality score of over 90% . however, the data in four languages falls short of the 90% quality standard . |
Can Language Models Follow Multiple Turns of Entangled Instructions? (2025.findings-emnlp)
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Chi Han, Xin Liu, Haodong Wang, Shiyang Li, Jingfeng Yang, Haoming Jiang, Zhengyang Wang, Qingyu Yin, Liang Qiu, Changlong Yu, Yifan Gao, Zheng Li, Bing Yin, Jingbo Shang, Heng Ji
| Challenge: | Despite of significant achievements in improving instruction-following capabilities of large language models, the ability to process multiple potentially entangled or conflicting instructions remains a considerable challenge. |
| Approach: | They construct multi-turn instruction with 1.1K high-quality multi-turned conversations using the human-in-the-loop approach and examine their capabilities. |
| Outcome: | The proposed model shows that it is difficult to integrate multiple turns and balance competing objectives when instructions intersect or conflict. |
Context-Driven and Reference-Guided Data Augmentation for Subtitle Translation (2026.findings-acl)
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| Challenge: | Large language models (LLMs) have demonstrated strong performance in translation tasks. |
| Approach: | They propose a method that expands source-side data by rewriting original subtitles using information that can be extracted from the context, such as character profiles and scene descriptions. |
| Outcome: | The proposed method improves BLEU scores for film subtitle translation and achieves superior stylistic quality in human evaluation. |