Challenge: Parallel sentence extraction is a task addressing the data sparsity problem found in multilingual natural language processing applications.
Approach: They propose a bidirectional recurrent neural network based approach to extract parallel sentences from multilingual corpora.
Outcome: The proposed approach outperforms existing approaches on noisy parallel corpora and shows significant improvements in translation performance.

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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.
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.
Deep Neural Networks at the Service of Multilingual Parallel Sentence Extraction (C18-1)

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Challenge: Existing models for parallel data harvesting from Wikipedia are language-independent, robust and highly scalable.
Approach: They propose an end-to-end neural model for large-scale parallel data harvesting from Wikipedia . their model is language-independent, robust, and highly scalable .
Outcome: The proposed model is language-independent, robust, and highly scalable.
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.
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.
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.
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 .
Sentence Concatenation Approach to Data Augmentation for Neural Machine Translation (2021.naacl-srw)

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Challenge: Neural machine translation is known to show poor performance at long sentence translations . however, when the sentence length exceeds a certain value, the quality of NMT becomes inferior to that of statistical machine translation.
Approach: They propose a method that uses given parallel corpora as train data to generate long sentences by concatenating two sentences at random.
Outcome: The proposed method improves translation quality more when combined with back-translation.
Searching for Effective Neural Extractive Summarization: What Works and What’s Next (P19-1)

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Challenge: Recent years have seen success in the use of deep neural networks on text summarization, but there is no clear understanding of why they perform so well or how they might be improved.
Approach: They propose to use different types of model architectures to improve extractive summarization systems.
Outcome: The proposed framework achieves state-of-the-art on CNN/DailyMail by a large margin based on observations and analysis.
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.

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