| Challenge: | Parallel sentence pairs are sentences that are translations of each other and convey the same meaning in the source and target languages. |
| Approach: | They propose a model which detects meaning divergences in parallel sentence pairs . parallel sentence pair are translations of each other, therefore often assumed to convey the same meaning . |
| Outcome: | The proposed model detects divergences more accurately than models based on word alignments. |
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Fixing Translation Divergences in Parallel Corpora for Neural MT (D18-1)
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| Challenge: | Existing methods to detect translation divergences from parallel corpora are noisy and limited in size. |
| Approach: | They propose an unsupervised method for detecting translation divergences in parallel sentences . they use a neural network that computes cross-lingual sentence similarity scores . |
| Outcome: | The proposed method improves translation performance for English-French and English-German translation tasks. |
Beyond Noise: Mitigating the Impact of Fine-grained Semantic Divergences on Neural Machine Translation (2021.acl-long)
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| Challenge: | Prior work treats all types of mismatches between source and target as noise . Consequently, it remains unclear how noisy parallel training samples impact NMT training. |
| Approach: | They propose a divergent-aware NMT framework that uses factors to help NMT recover from the degradation caused by naturally occurring divergences. |
| Outcome: | The proposed framework improves translation quality and model calibration on EN-FR tasks. |
Detecting Fine-Grained Cross-Lingual Semantic Divergences without Supervision by Learning to Rank (2020.emnlp-main)
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| Challenge: | Detecting fine-grained differences in content conveyed in different languages is expensive and hard to scale. |
| Approach: | They propose a training strategy for multilingual BERT models by learning to rank divergent examples of varying granularity. |
| Outcome: | The proposed model improves the prediction and annotation of fine-grained semantic divergences. |
Parallel Sentence Mining by Constrained Decoding (2020.acl-main)
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| Challenge: | Using a neural machine translation system to extract parallel sentences from two monolingual corpora is challenging. |
| Approach: | They propose a method to extract parallel sentences from two monolingual corpora by constraining the decoding by a prefix tree built on the other corpus. |
| Outcome: | The proposed method achieves comparable results to other submissions on the BUCC shared task. |
Fine-Grained Analysis of Cross-Linguistic Syntactic Divergences (2020.acl-main)
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Dmitry Nikolaev, Ofir Arviv, Taelin Karidi, Neta Kenneth, Veronika Mitnik, Lilja Maria Saeboe, Omri Abend
| Challenge: | Existing work on quantifying the prevalence of syntactic divergences across languages has not been done. |
| Approach: | They propose a framework for extracting divergence patterns for any language pair from a parallel corpus building on Universal Dependencies. |
| Outcome: | The proposed framework provides a detailed picture of cross-language divergences, generalizes previous approaches, and lends itself to full automation. |
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. |
Neural Machine Translation for Bilingually Scarce Scenarios: a Deep Multi-Task Learning Approach (N18-1)
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| Challenge: | Neural machine translation requires large amount of parallel training text to learn a reasonable quality translation model. |
| Approach: | They propose a multi-task learning approach that leverages monolingual linguistic resources in the source side of a machine translation task. |
| Outcome: | The proposed approach is effective on three translation tasks: English-to-French, English- to-Farsi, and English-à-Vietnamese. |
Addressing word-order Divergence in Multilingual Neural Machine Translation for extremely Low Resource Languages (N19-1)
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| Challenge: | Existing studies show that transfer learning works best when the languages are related. |
| Approach: | They propose to pre-order assisting language sentences to match the word order of the source language and train the parent model. |
| Outcome: | The proposed model can improve translation quality in low-resource scenarios by pre-ordering the assisting language sentences to match the word order of the source language and training the parent model. |
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. |
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A Stochastic Decoder for Neural Machine Translation (P18-1)
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| Challenge: | Neural machine translation models do not account for local lexical and syntactic variation in parallel corpora. |
| Approach: | They propose a deep generative model of machine translation which incorporates a chain of latent variables to account for local lexical and syntactic variation in parallel corpora. |
| Outcome: | The proposed model consistently improves over strong baselines on several different language pairs. |