Papers with word alignment
Salute the Classic: Revisiting Challenges of Machine Translation in the Age of Large Language Models (2025.tacl-1)
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| Challenge: | a recent study revisits six core challenges that have influenced the evolution of Neural Machine Translation (NMT) domain mismatch, amount of parallel data, rare word prediction, translation of long sentences and sub-optimal beam search remain challenges in LLMs. |
| Approach: | They revisit core challenges that have acted as benchmarks for progress in NMT . they propose to revisit these challenges and offer insights into their relevance . |
| Outcome: | The proposed models significantly improve translation of sentences containing approximately 80 words, even translating documents up to 512 words. |
LanguageNet: Learning to Find Sense Relevant Example Sentences (C18-2)
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| Challenge: | LanguageNet is a system that can help second language learners to search for different meanings and usages of a word . the polysemy of words, namely words with more than one sense, is one of the major challenges for ESOL learners . |
| Approach: | They propose a system which can help second language learners to search for different meanings of a word. |
| Outcome: | The proposed system can help second language learners to search for different meanings and usages of a word. |
Multi-lingual Common Semantic Space Construction via Cluster-consistent Word Embedding (D18-1)
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| Challenge: | a new approach to multilingual word embedding is needed to achieve this goal . a multilingual common semantic space is a language-agnostic semantic continuous space . |
| Approach: | They propose a multilingual common semantic space where words from multiple languages are mapped into a shared space so that resources and knowledge can be shared across languages. |
| Outcome: | The proposed approach achieves 14.6% absolute F-score gain over state-of-the-art methods on cross-lingual direct transfer. |
Bootstrapping Multilingual AMR with Contextual Word Alignments (2021.eacl-main)
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Janaki Sheth, Young-Suk Lee, Ramón Fernandez Astudillo, Tahira Naseem, Radu Florian, Salim Roukos, Todd Ward
| Challenge: | Abstract Meaning Representation (AMR) is a sentence-level graph that is biased towards English. |
| Approach: | They propose a technique for foreign-text-to-English AMR alignment using contextual word alignment between English and foreign language tokens. |
| Outcome: | The proposed technique outperforms the best results for German, Italian, Spanish and Chinese. |
Do GPTs Produce Less Literal Translations? (2023.acl-short)
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| Challenge: | Large Language Models (LLMs) are general-purpose language models capable of many natural language generation or understanding tasks. |
| Approach: | They investigate how LLMs differ qualitatively from standard Neural Machine Translation models by measuring literalness and monotonicity. |
| Outcome: | The proposed models achieve close to state-of-the-art translation performance under few-shot prompting . the results are backed up by human evaluations and a newer MT quality metrics . |
An Untold Story of Preprocessing Task Evaluation: An Alignment-based Joint Evaluation Approach (2024.lrec-main)
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Eunkyul Leah Jo, Angela Yoonseo Park, Grace Tianjiao Zhang, Izia Xiaoxiao Wang, Junrui Wang, MingJia Mao, Jungyeul Park
| Challenge: | a preprocessing task such as tokenization and sentence boundary detection (SBD) has been considered as a solution to many NLP challenges . however, the low error rates of current methods are mainly specific to certain tasks and rule-based tokenization can be difficult to use across different systems. |
| Approach: | They propose an evaluation algorithm that combines both tokenization and SBD results to improve evaluation reliability. |
| Outcome: | The proposed evaluation algorithm improves the reliability of evaluations by reevaluating the counts of true positive cases for F1 measures in both preprocessing tasks jointly. |
On the Word Alignment from Neural Machine Translation (P19-1)
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| Challenge: | Prior researches suggest that neural machine translation (NMT) captures word alignment through its attention mechanism, however, attention may fail to capture word alignment for some NMT models. |
| Approach: | They propose two methods to induce word alignment which are general and agnostic to specific NMT models. |
| Outcome: | The proposed methods induce much better word alignment than attention. |
When do Contrastive Word Alignments Improve Many-to-many Neural Machine Translation? (2022.findings-naacl)
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| Challenge: | Existing methods to improve pre-training for many-to-many neural machine translation use manual cleaning of bilingual dictionaries, which are unavailable for most language pairs. |
| Approach: | They propose a word-level contrastive objective to leverage word alignments for many-to-many neural machine translation (NMT) Empirical results show that this leads to 0.8 BLEU gains for several language pairs. |
| Outcome: | Empirical results show that the proposed objective leads to 0.8 BLEU gains for several language pairs. |
End-to-End Neural Word Alignment Outperforms GIZA++ (2020.acl-main)
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| Challenge: | Word alignment was once a core unsupervised learning task in natural language processing . but word alignment still plays an important role in interactive applications of neural machine translation, such as annotation transfer and lexicon injection. |
| Approach: | They propose to use a Transformer model to train an unsupervised word alignment model. |
| Outcome: | The proposed method outperforms GIZA++ on three data sets and is tightly integrated and does not affect translation quality. |
SimAlign: High Quality Word Alignments Without Parallel Training Data Using Static and Contextualized Embeddings (2020.findings-emnlp)
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| Challenge: | Word alignments are useful for statistical and neural machine translation (NMT) and cross-lingual annotation projection. |
| Approach: | They propose to leverage multilingual word embeddings for word alignment. |
| Outcome: | The proposed methods perform better for four languages and comparable for two languages than traditional statistical aligners even with abundant parallel data. |
Assessing Non-autoregressive Alignment in Neural Machine Translation via Word Reordering (2022.findings-emnlp)
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| Challenge: | Existing non-autoregressive neural machine translation models that implicitly model dependencies are sub-optimal in handling word order errors. |
| Approach: | They propose to learn a non-autoregressive language model that can be combined with Viterbi decoding to achieve better reordering performance. |
| Outcome: | The proposed model outperforms state-of-the-art reordering mechanisms under different word permutation settings with a 2-27 BLEU improvement, suggesting high potential for word alignment in NAT. |
Word Alignment by Fine-tuning Embeddings on Parallel Corpora (2021.eacl-main)
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| Challenge: | Existing work on word alignment has focused on unsupervised learning on parallel text. |
| Approach: | They propose to combine pre-trained contextualized word embeddings with multilingually trained language models to achieve competitive results on word alignment tasks. |
| Outcome: | The proposed model outperforms state-of-the-art models on five language pairs and can train multilingual word aligners that can obtain robust performance on different language pairs. |
Word Alignment as Preference for Machine Translation (2024.emnlp-main)
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| Challenge: | Hallucination and omission are a problem in machine translation because of an LLM's size and low-resource languages. |
| Approach: | They propose to use word alignment as preference to optimize an LLM-based MT model to mitigate hallucination and omission problems. |
| Outcome: | The proposed model is able to mitigate hallucination and omission by using word alignment as preference. |
Unbalanced Optimal Transport for Unbalanced Word Alignment (2023.acl-long)
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| Challenge: | Figure 1 illustrates the challenges of monolingual word alignment. |
| Approach: | They propose to use the family of optimal transport (OT) to achieve unbalanced word alignment that values alignment and null alignment on unsupervised datasets. |
| Outcome: | The proposed methods are competitive against the state-of-the-art methods on challenging datasets with high null alignment frequencies. |
Third-Party Aligner for Neural Word Alignments (2022.findings-emnlp)
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| Challenge: | Existing work shows that word alignment can be competitive . |
| Approach: | They propose to use word alignments generated by a third-party word aligner to supervise the neural word alignment training. |
| Outcome: | The proposed approach can find more accurate word alignments and delete wrong alignments, leading to better performance than the current best third-party word aligner. |
Word Rotator’s Distance (2020.emnlp-main)
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| Challenge: | Existing approaches to measure textual similarity are inconsistent with the word alignment and are empirically inferior to the simple cosine similarity between general-purpose sentence vectors. |
| Approach: | They propose to decouple word vectors into their norm and direction and then grow the norm and directions of word vector. |
| Outcome: | The proposed methods outperform alignment-based approaches on several benchmarks and strong baselines on the semantic textual similarity task. |
Cross-language Sentence Selection via Data Augmentation and Rationale Training (2021.acl-long)
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| Challenge: | a new approach to cross-language sentence selection is proposed for low-resource contexts . a cross-lingual embedding-based model is proposed that avoids translation entirely . |
| Approach: | They propose a cross-lingual embedding-based query relevance model that uses data augmentation and negative sampling techniques to directly learn a query-sentence pair. |
| Outcome: | The proposed approach performs better than state-of-the-art models on noisy parallel data . consistent improvements are seen across three language pairs over state- of-the art models . |
Structural Supervision for Word Alignment and Machine Translation (2022.findings-acl)
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| Challenge: | Existing knowledge on syntactic structure neglects the rich structural information from target tokens and the structural similarity between the source and target sentences. |
| Approach: | They propose to incorporate syntactic structure of both source and target tokens into the encoder-decoder framework, tightly correlating the internal logic of word alignment and machine translation for multi-task learning. |
| Outcome: | The proposed method outperforms baselines on four publicly available language pairs and consistently outperformed baselines in alignment accuracy and translation quality. |
Frustratingly Easy Label Projection for Cross-lingual Transfer (2023.findings-acl)
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| Challenge: | Existing approaches to improve cross-lingual transfer performance are based on word alignment, but no empirical studies have evaluated their effectiveness or limitations. |
| Approach: | They propose a mark-then-translate method that integrates translation and projection by inserting special markers around the labeled spans in the original sentence. |
| Outcome: | The proposed method outperforms word alignment-based methods in 57 languages and three tasks. |
Using English Baits to Catch Serbian Multi-Word Terminology (L18-1)
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| Challenge: | a new method for bilingual terminology extraction is proposed for a source language and a target language. |
| Approach: | They propose to use a bilingual terminology extraction approach for a source language and a target language to extract the terminology for sri lanka. |
| Outcome: | The proposed method extracts terminology for a source language and a target language from it. |
Semi-Automated Construction of Sense-Annotated Datasets for Practically Any Language (2025.coling-main)
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Jai Riley, Bradley M. Hauer, Nafisa Sadaf Hriti, Guoqing Luo, Amir Reza Mirzaei, Ali Rafiei, Hadi Sheikhi, Mahvash Siavashpour, Mohammad Tavakoli, Ning Shi, Grzegorz Kondrak
| Challenge: | Word sense disambiguation is a widely studied NLP task of identifying the meaning of a word in context. |
| Approach: | They propose a method to create parallel sense-annotated datasets in English . they use machine translation, word alignment, sense projection, and sense filtering to produce silver annotations . |
| Outcome: | The proposed method produces parallel sense-annotated datasets on Farsi, Chinese, and Bengali . the results are higher than those obtained with recent multilingual systems, the authors say . |
Jointly Learning to Align and Translate with Transformer Models (D19-1)
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| Challenge: | Existing word alignment models are not accurate for word alignments. |
| Approach: | They propose a method to train a Transformer model to produce accurate translations and alignments. |
| Outcome: | The proposed model outperforms GIZA++ trained models on translation and alignment tasks while maintaining translation accuracy. |
BinaryAlign: Word Alignment as Binary Sequence Labeling (2024.acl-long)
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| Challenge: | State-of-the-art word alignment training methods require a different class depending on the availability of gold data for a particular language pair. |
| Approach: | They propose a novel word alignment technique based on binary sequence labeling that outperforms existing approaches in both scenarios. |
| Outcome: | The proposed method outperforms existing models on non-English language pairs and performs stratified error analysis over alignment error type. |
Attention is Not Only a Weight: Analyzing Transformers with Vector Norms (2020.emnlp-main)
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| Challenge: | Attention is a key component of Transformers, which have achieved considerable success in natural language processing. |
| Approach: | They propose to integrate attention weights and the norm of transformed input vectors into a norm-based analysis that incorporates the norm. |
| Outcome: | The proposed analysis shows that attention weights alone determine the output of attention and that reasonable word alignment can be extracted from attention mechanisms of Transformers. |
Towards Unsupervised Recognition of Token-level Semantic Differences in Related Documents (2023.emnlp-main)
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| Challenge: | Existing studies on word-level predictions and highlighting semantic differences in natural language documents did not focus on semantic differences as the main target. |
| Approach: | They propose to perform a token-level regression task to highlight semantic differences between two documents . they use word alignment and sentence-level contrastive learning to evaluate the approaches . |
| Outcome: | The proposed approach has a robust correlation to gold labels, but all unsupervised approaches leave a margin of improvement. |
The Arabic Generality Score: Another Dimension of Modeling Arabic Dialectness (2025.emnlp-main)
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| Challenge: | Recent work addresses this issue by modeling dialectness as a continuous variable . however, ALDi reduces complex variation to a single dimension . |
| Approach: | They propose a way to model Arabic dialectness as a continuous variable . they propose etymology-aware edit distance and a regression model to model AGS . |
| Outcome: | The proposed approach outperforms baselines on a multi-dialect benchmark. |
Mending the Holes: Mitigating Reward Hacking in Reinforcement Learning for Multilingual Translation (2026.findings-acl)
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| Challenge: | Existing methods for training large language models rely heavily on high-quality parallel data, which are often scarce or unavailable for low-resource languages. |
| Approach: | They propose a reinforcement training method using only monolingual text to elevate LLMs’ translation capabilities on massive low-resource languages while retaining their performance on high-resourced languages. |
| Outcome: | The proposed model outperforms LLaMAX, one of the strongest open-source multilingual LLMs on 1,414 language directions on Flores-101 dataset. |