CHINESE-ENGLISH

Better Chinese Sentence Segmentation with Reinforcement Learning (2021.findings-acl)

Challenge:	Existing neural machine translation models are not able to translate dialogues in real life scenarios.
Approach:	They propose a joint learning method to identify omission and typos and utilize context to translate dialogue utterances.
Outcome:	The proposed method improves translation quality by 3.2 BLEU over baselines and recovers omitted pronouns by 47.16%.

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Srivatsan Srinivasan, Chris Dyer

Multi-Domain Neural Machine Translation with Word-Level Domain Context Discrimination (D18-1)

Challenge:	Chinese-English machine translation systems use ambiguous sentence boundaries, but English and Chinese use different orthographic conventions to designate sentence boundaries.
Approach:	They propose a segmentation policy that splits Chinese texts into segments that can be independently translated to maximise translation quality.
Outcome:	The proposed method improves the baseline BLEU score on the Chinese-English news translation task by +0.3 BLUE overall and the score on input segments that contain more than 60 words by +3 BL EU.

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Jiali Zeng, Jinsong Su, Huating Wen, Yang Liu, Jun Xie, Yongjing Yin, Jianqiang Zhao

A Supervised Word Alignment Method based on Cross-Language Span Prediction using Multilingual BERT (2020.emnlp-main)

Challenge:	Experimental results on Chinese-English and English-French multi-domain translation tasks demonstrate the effectiveness of the proposed model.
Approach:	They propose to use mixed-domain parallel sentences to construct a unified model that allows translation to switch between different domains.
Outcome:	The proposed model distinguishes and exploits word-level domain contexts on Chinese-English and English-French translation tasks.

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Masaaki Nagata, Katsuki Chousa, Masaaki Nishino

Semantic Text Processing

Challenge:	supervised word alignment tools such as GIZA++, MGIZA (Gao and Vogel, 2008) and FastAlign remain stagnant in terms of word alignment accuracy.
Approach:	They propose a supervised word alignment method based on cross-language span prediction by formalizing a word alignment problem as a collection of independent predictions from a token in the source sentence to a span in the target sentence.
Outcome:	The proposed method significantly outperforms previous supervised and unsupervised word alignment methods without any bitexts for pretraining.

Semi-Autoregressive Neural Machine Translation (D18-1)

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Chunqi Wang, Ji Zhang, Haiqing Chen

A Simple and Effective Approach to Coverage-Aware Neural Machine Translation (P18-2)

Challenge:	Existing approaches to neural machine translation are typically autoregressive but suffer from low parallelizability and thus slow at decoding long sequences.
Approach:	They propose a semi-autoregressive Transformer model for fast sequence generation that keeps the autoregressive property in global but relieves in local .
Outcome:	The proposed model achieves 5.58 speedup while maintaining 88% translation quality, significantly better than previous non-autoregressive methods.

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Yanyang Li, Tong Xiao, Yinqiao Li, Qiang Wang, Changming Xu, Jingbo Zhu

Adaptive Multi-pass Decoder for Neural Machine Translation (D18-1)

Challenge:	Neural Machine Translation (NMT) models are used to solve translation problems using long-term models.
Approach:	They propose a method to seek a better balance between model confidence and length preference for Neural Machine Translation.
Outcome:	The proposed model improves on Chinese-English and English-German translation tasks.

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Xinwei Geng, Xiaocheng Feng, Bing Qin, Ting Liu

Bag-of-Words as Target for Neural Machine Translation (P18-2)

Challenge:	End-to-end neural machine translation (NMT) has attracted increasing attention in recent years.
Approach:	They propose an adaptive multi-pass decoder which introduces a flexible multi- pass polishing mechanism to extend the capacity of NMT via reinforcement learning.
Outcome:	The proposed architecture improves Chinese-English translation with 1.55 BLEU . the proposed architecture adopts a flexible multi-pass polishing mechanism .

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Shuming Ma, Xu Sun, Yizhong Wang, Junyang Lin

Bridging Cultures in the Kitchen: A Framework and Benchmark for Cross-Cultural Recipe Retrieval (2024.emnlp-main)

Challenge:	Existing neural machine translation models only use one correct sentence as the target, and the other correct sentences are punished as the incorrect ones.
Approach:	They propose an approach that uses both the sentences and the bag-of-words as targets in the training stage to encourage the model to generate the potentially correct sentences that are not appeared in the train set.
Outcome:	The proposed model outperforms baseline models on a Chinese-English translation dataset by the BLEU score of 4.55.

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Tianyi Hu, Maria Maistro, Daniel Hershcovich

Information Retrieval

Challenge:	Adapting recipes to cultural differences presents significant importance and challenges . bridging cultural differences is a challenge, but IR can help.
Approach:	They propose a framework that preserves the original recipe and its cultural appropriateness for the target culture.
Outcome:	The proposed framework preserves the original recipe and its cultural appropriateness for the target culture while maintaining relevance to the original.

Improving Back-Translation with Uncertainty-based Confidence Estimation (D19-1)

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Shuo Wang, Yang Liu, Chao Wang, Huanbo Luan, Maosong Sun

Iterative Dual Domain Adaptation for Neural Machine Translation (D19-1)

Challenge:	Despite the success of low-resource neural machine translation, there is a data scarcity problem in many languages . large-scale, high-quality, and widecoverage bilingual corpora do not exist for most language pairs .
Approach:	They propose to quantify confidence of NMT models based on model uncertainty . they propose to use uncertainty-based confidence measures to improve back-translation .
Outcome:	The proposed model outperforms conventional statistical machine translation (SMT) on Chinese-English and English-German translation tasks.

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Jiali Zeng, Yang Liu, Jinsong Su, Yubing Ge, Yaojie Lu, Yongjing Yin, Jiebo Luo

Responsible & Trustworthy NLP

Challenge:	Empirical results on Chinese-English and English-German translation tasks demonstrate the effectiveness of our proposed framework.
Approach:	They propose an iterative dual domain adaptation framework for neural machine translation that uses multiple corpora to perform bidirectional translation knowledge transfer.
Outcome:	Empirical results on Chinese-English and English-German translation tasks demonstrate the effectiveness of the proposed framework.

Multi-agent Learning for Neural Machine Translation (D19-1)

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Tianchi Bi, Hao Xiong, Zhongjun He, Hua Wu, Haifeng Wang

Generating Diverse Translation from Model Distribution with Dropout (2020.emnlp-main)

Challenge:	Experimental results show that training with more than one agent improves translation quality and improves accuracy.
Approach:	They propose to introduce diverse agents in an in- teractive updating process to train NMT models with an additional agent.
Outcome:	The proposed approach improves on NIST Chinese-English, IWSLT 2014 German- English, WMT 2014 English-German translation tasks and shows competitive performance on all tasks.

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Xuanfu Wu, Yang Feng, Chenze Shao

One Model to Learn Both: Zero Pronoun Prediction and Translation (D19-1)

Challenge:	Existing neural machine translation models lack diversity in their generation.
Approach:	They propose to generate diverse translations by deriving Bayesian models and sampling models from them for inference.
Outcome:	The proposed method makes a better trade-off between diversity and accuracy.

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Longyue Wang, Zhaopeng Tu, Xing Wang, Shuming Shi

Revisit Automatic Error Detection for Wrong and Missing Translation – A Supervised Approach (D19-1)

Challenge:	Zero pronouns (ZPs) are often omitted in pro-drop languages, but should be recalled in non-pro-drop language.
Approach:	They propose a unified and discourse-aware ZP translation approach for neural MT models . they jointly learn to predict and translate ZPs in an end-to-end manner .
Outcome:	The proposed method improves translation performance and ZP prediction accuracy over baseline models and external models.

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Wenqiang Lei, Weiwen Xu, Ai Ti Aw, Yuanxin Xiang, Tat Seng Chua

Syntax-Enhanced Neural Machine Translation with Syntax-Aware Word Representations (N19-1)

Challenge:	Current machine translation techniques are bottlenecked by adequacy issues . we propose automatic detection of missing and wrong translations .
Approach:	They propose automatic detection of adequacy errors in MT hypothesis for MT model evaluation by annotating missing and wrong translations in 15000 Chinese-English translation pairs.
Outcome:	The proposed model can detect missing and wrong translations in 15000 Chinese-English translation pairs.

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Meishan Zhang, Zhenghua Li, Guohong Fu, Min Zhang

Semantic Text Processing

Challenge:	Syntax integration has been demonstrated highly effective in neural machine translation (NMT).
Approach:	They propose a method to integrate source-side syntax implicitly for neural machine translation . they use hidden representations of a well-trained end-to-end dependency parser to concatenate them with ordinary word embeddings to enhance basic NMT models.
Outcome:	The proposed method outperforms existing methods on two translation tasks . it can be easily integrated into the widely-used sequence-to-sequence (Seq2Sequen) framework .

Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets (N18-1)

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Zhen Yang, Wei Chen, Feng Wang, Bo Xu

Neural Machine Translation with Decoding History Enhanced Attention (C18-1)

Challenge:	Experimental results show that the proposed model consistently outperforms the traditional RNNSearch and the newly emerged state-of-the-art Transformer on English-German and Chinese-English translation tasks.
Approach:	They propose an approach for applying GANs to NMT by building a conditional sequence generative adversarial net with two adversarials.
Outcome:	The proposed model outperforms the existing RNNSearch and Transformer on English-German and Chinese-English translation tasks.

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Mingxuan Wang, Jun Xie, Zhixing Tan, Jinsong Su, Deyi Xiong, Chao Bian

Shallow Discourse Annotation for Chinese TED Talks (2020.lrec-1)

Challenge:	Neural machine translation with source-side attention has been criticized for its poor memory performance.
Approach:	They propose to use a Decoding History Enhanced Attention mechanism to render NMT models better at selecting both source-side and target-side information.
Outcome:	The proposed model improves by 0:9 BLEU on Chinese-English translation and the state-of-the-art on a larger task.

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Wanqiu Long, Xinyi Cai, James Reid, Bonnie Webber, Deyi Xiong

Challenge:	Existing methods to annotate text with discourse properties are limited to newspaper articles and are not available in Chinese.
Approach:	They propose to annotate TED talks with Chinese-related properties using the Penn Discourse TreeBank annotation style . they propose to use planned monologues instead of written text to annnotate Chinese-specific properties.
Outcome:	The proposed method is able to achieve reliable results in Chinese spoken monologues, and is based on the Penn Discourse TreeBank annotation style.

PROSE: A Pronoun Omission Solution for Chinese-English Spoken Language Translation (2023.emnlp-main)

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Ke Wang, Xiutian Zhao, Yanghui Li, Wei Peng

HABLex: Human Annotated Bilingual Lexicons for Experiments in Machine Translation (D19-1)

Challenge:	Pro-drop (‘pronoun-dropping’) language requires NMT systems to recover omitted pronouns, but this task lacks sufficient datasets for benchmarking .
Approach:	They propose a benchmarking method that leverages the semantic embedding of dropped pronouns to augment training pairs to alleviate the negative impact introduced by pro-drop .
Outcome:	The proposed method outperforms existing methods regarding omitted pronoun retrieval and overall translation quality on four Chinese-English translation corpora.

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Brian Thompson, Rebecca Knowles, Xuan Zhang, Huda Khayrallah, Kevin Duh, Philipp Koehn

Alternated Training with Synthetic and Authentic Data for Neural Machine Translation (2021.findings-acl)

Challenge:	Existing methods to incorporate bilingual lexicons into statistical machine translation are unclear how to do so in the neural framework.
Approach:	They present a dataset to test methods for bilingual lexicon integration into neural machine translation using human generated alignments of words and phrases in three language pairs.
Outcome:	The proposed method improves on baselines and improves training to address overfitting.

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Rui Jiao, Zonghan Yang, Maosong Sun, Yang Liu

Towards Robust Neural Machine Translation (P18-1)

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.

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Yong Cheng, Zhaopeng Tu, Fandong Meng, Junjie Zhai, Yang Liu

Learning Adaptive Segmentation Policy for Simultaneous Translation (2020.emnlp-main)

Challenge:	Small perturbations in the input can severely distort intermediate representations and thus impact translation quality of neural machine translation models.
Approach:	They propose adversarial stability training to make encoder and decoder robust to perturbations by enabling them to behave similarly for the original input and its perturbed counterpart.
Outcome:	The proposed approach improves translation quality and robustness over strong models on Chinese-English, English-German and English-French translation tasks.

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Ruiqing Zhang, Chuanqiang Zhang, Zhongjun He, Hua Wu, Haifeng Wang

Exploring Better Text Image Translation with Multimodal Codebook (2023.acl-long)

Challenge:	Experimental results show that adaptive segmentation policies for simultaneous translation are more accurate than current methods . if translation starts before adequate source content is delivered, the quality of translation degrades . waiting for too much source text increases latency, which would hurt accuracy .
Approach:	They propose a new adaptive segmentation policy for simultaneous translation based on human interpreters . it learns to segment the source text by considering possible translations produced by the translation model .
Outcome:	Experimental results show that the proposed method achieves better accuracy-latency trade-off over state-of-the-art methods.

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Zhibin Lan, Jiawei Yu, Xiang Li, Wen Zhang, Jian Luan, Bin Wang, Degen Huang, Jinsong Su

Multimodality

Challenge:	Current studies on text image translation face bottlenecks due to lack of a publicly available dataset and poor optical character recognition.
Approach:	They propose a text image translation model with a multimodal codebook and an OCR dataset for Chinese-English translation.
Outcome:	The proposed model can associate the image with relevant texts, providing useful supplementary information for translation.

Guiding Teacher Forcing with Seer Forcing for Neural Machine Translation (2021.acl-long)

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Yang Feng, Shuhao Gu, Dengji Guo, Zhengxin Yang, Chenze Shao

Progressive Multi-Granularity Training for Non-Autoregressive Translation (2021.findings-acl)

Challenge:	Neural machine translation models are usually based on attention-based encoder-decoder frameworks.
Approach:	They introduce a seer decoder into the encoder-decoder framework during training . they force the conventional decoded decodes to simulate the behavior of the seer .
Outcome:	The proposed method outperforms baselines on Chinese, English and German translation tasks.

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Liang Ding, Longyue Wang, Xuebo Liu, Derek F. Wong, Dacheng Tao, Zhaopeng Tu

Video-Helpful Multimodal Machine Translation (2023.emnlp-main)

Challenge:	Non-autoregressive translation models are weak at learning high-mode knowledge, argues a new study . despite the improved learning difficulty, there are still complicated word orders and structures in the synthetic sentences, making the NAT performance sub-optimal.
Approach:	They propose to train non-autoregressive translation models to learn fine-grained lower-mode knowledge . they break down sentence-level examples into three types and increase granularities .
Outcome:	The proposed method improves phrase translation accuracy and model reordering ability against strong NAT baselines.

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Yihang Li, Shuichiro Shimizu, Chenhui Chu, Sadao Kurohashi, Wei Li

Multimodality

Challenge:	Existing multimodal machine translation datasets contain images and video captions or instructional video subtitles, which rarely contain linguistic ambiguity.
Approach:	They propose an MMT dataset that contains ambiguous subtitles and a video-helpful evaluation set.
Outcome:	The proposed model performs significantly better than existing models on ambiguous subtitles dataset . it is based on a training set and video-helpful evaluation set .

Guiding Large Language Models to Post-Edit Machine Translation with Error Annotations (2024.findings-naacl)

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Dayeon Ki, Marine Carpuat

Fine-grained Coordinated Cross-lingual Text Stream Alignment for Endless Language Knowledge Acquisition (D18-1)

Challenge:	supervised systems have not replaced dedicated supervised models for machine translation tasks.
Approach:	They propose to guide LLMs to post-edit MT with feedback from MQM annotations . they then fine-tune the LLM to improve its ability to exploit the feedback .
Outcome:	The proposed model improves TER, BLEU and COMET scores on Chinese-English, English-German and English-Russian data.

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Tao Ge, Qing Dou, Heng Ji, Lei Cui, Baobao Chang, Zhifang Sui, Furu Wei, Ming Zhou

Reference Network for Neural Machine Translation (P19-1)

Challenge:	a novel information network decipherment paradigm is proposed for fine-grained coordinated cross-lingual text stream alignment.
Approach:	They propose to use Burst Information Networks as media to represent text streams . they propose a simple yet effective information network decipherment algorithm with diverse clues .
Outcome:	The proposed approach outperforms existing approaches on bilingual lexicon extraction from coordinated text streams and can harvest high-quality alignments from large amounts of streaming data for endless language knowledge mining.

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Han Fu, Chenghao Liu, Jianling Sun

Point, Disambiguate and Copy: Incorporating Bilingual Dictionaries for Neural Machine Translation (2021.acl-long)

Challenge:	Neural Machine Translation (NMT) generates translations in isolation, resulting in translation inconsistency and ambiguity.
Approach:	They propose to incorporate referring process into translation decoding of NMT by using local coordinates coding to obtain global context vectors containing monolingual and bilingual contextual information.
Outcome:	The proposed model improves translation quality with lightweight computation cost on Chinese-English and English-German translation tasks.

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Tong Zhang, Long Zhang, Wei Ye, Bo Li, Jinan Sun, Xiaoyu Zhu, Wen Zhao, Shikun Zhang

Multi-Task Learning for Cross-Lingual Abstractive Summarization (2022.lrec-1)

Challenge:	Existing approaches to incorporate bilingual dictionaries into Neural Machine Translation (NMT) models have been criticized for lack of integration of bilingual lexical information into the neural architecture.
Approach:	They propose a neural architecture to incorporate bilingual dictionaries into Neural Machine Translation models by introducing three new components: Pointer, Disambiguator, and Copier.
Outcome:	The proposed method achieves the following merits inherently compared with previous efforts: (1) Pointer leverages the semantic information from bilingual dictionaries, for the first time, to better locate source words whose translation in dictionary can potentially be used; (2) Disambiguator synthesizes contextual information from source view and target view, both of which contribute to distinguishing translation of a specific source word from multiple candidates in dicaries; (3) Copier systematically connects Pointer and Disambiguators based on a hierarchical

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Sho Takase, Naoaki Okazaki

Surprisal Predicts Code-Switching in Chinese-English Bilingual Text (2020.emnlp-main)

Challenge:	Existing studies use pseudo cross-lingual abstractive summarization data to train neural encoder-decoders.
Approach:	They propose a multi-task learning framework for cross-lingual abstractive summarization that attaches a special token to the beginning of the input sentence to indicate the target task.
Outcome:	The proposed model achieves better performance than the model trained with only pseudo cross-lingual abstractive summarization data.

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Jesús Calvillo, Le Fang, Jeremy Cole, David Reitter

Learning When to Concentrate or Divert Attention: Self-Adaptive Attention Temperature for Neural Machine Translation (D18-1)

Challenge:	a new study examines the propensity of bilinguals to switch languages . word surprisal and word entropy are important predictors of code-switching .
Approach:	They propose high cognitive effort as a reason for code-switching . they use a computational model of surprisal and word entropy to model code-changing .
Outcome:	The proposed model shows that word surprisal, but not entropy, is a significant predictor . sentence length is also a predictor, which has been related to sentence complexity .

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Junyang Lin, Xu Sun, Xuancheng Ren, Muyu Li, Qi Su

A Deep Analysis of the Impact of Multiword Expressions and Named Entities on Chinese-English Machine Translations (2024.findings-emnlp)

Challenge:	Neural Machine Translation models treat decoding at each time step equally with the same matrix . conventional methods treat decoder outputs at all time steps with the identical weight matrix causing inaccuracy .
Approach:	They propose a model with a mechanism to control the softness of attention by means of an attention temperature.
Outcome:	The proposed model outperforms baseline models on Chinese-English and English-Vietnamese translations.

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Huacheng Song, Hongzhi Xu

MultiMWE: Building a Multi-lingual Multi-Word Expression (MWE) Parallel Corpora (2020.lrec-1)

Challenge:	a study on the impact of multiword expressions and multiword named entities (NEs) on the performance of Chinese-English machine translation systems is presented.
Approach:	They propose to use Chinese multiword expressions and multiword named entities (NEs) to evaluate machine translation performance.
Outcome:	The proposed methods show that Chinese-English machine translation systems perform significantly worse on Chinese sentences with most kinds of MWEs and NEs.

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Lifeng Han, Gareth Jones, Alan Smeaton

Token Drop mechanism for Neural Machine Translation (2020.coling-main)

Challenge:	Existing bilingual or multi-lingual MWE corpora are limited for multilingual use . only 871 pairs of English-German MWEs are available for research .
Approach:	They present a collection of bilingual and multi-lingual MWEs extracted from parallel corpora.
Outcome:	The available bilingual or multi-lingual MWE corpus is very limited . the collection is a small collection of 871 pairs of English-German MWEs .

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Huaao Zhang, Shigui Qiu, Xiangyu Duan, Min Zhang

Robust Neural Machine Translation with Doubly Adversarial Inputs (P19-1)

Challenge:	Neural machine translation models are vulnerable to unfamiliar inputs.
Approach:	They propose to drop tokens of the input sentences to improve generalization and avoid overfitting for the NMT model.
Outcome:	The proposed approach improves on Chinese-English and English-Romanian benchmarks and achieves significant performance improvements over baselines.

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Yong Cheng, Lu Jiang, Wolfgang Macherey

Robustness in NLP

Challenge:	Neural machine translation (NMT) models suffer from noisy perturbations in the input . a gradient-based method to craft adversarial examples informed by the translation loss is proposed .
Approach:	They propose an approach to improve the robustness of NMT models by attacking the translation model with adversarial source examples and defending the model with a target input.
Outcome:	The proposed approach improves translation performance and robustness on clean inputs and higher on noisy data.

Continual Knowledge Distillation for Neural Machine Translation (2023.acl-long)

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Yuanchi Zhang, Peng Li, Maosong Sun, Yang Liu

Document Sub-structure in Neural Machine Translation (2020.lrec-1)

Challenge:	Current parallel corpora are not publicly accessible but trained models are more readily available.
Approach:	They propose a method to take advantage of existing translation models to improve one model of interest.
Outcome:	The proposed method improves on Chinese-English and German-English datasets and is robust to malicious models.

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Radina Dobreva, Jie Zhou, Rachel Bawden

Rare but Severe Neural Machine Translation Errors Induced by Minimal Deletion: An Empirical Study on Chinese and English (2022.coling-1)

Challenge:	Current approaches to machine translation (MT) translate sentences in isolation, disregarding context they appear in, or model context at the level of the full document.
Approach:	They propose to include information about the topic of the section within which each sentence is found in a document that is not homogeneous . they use a cache-based model to model the context of the document, instead of translating sentences in isolation .
Outcome:	The proposed model incorporates information about the topic of the section within which each sentence is found into a neural model.

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Ruikang Shi, Alvin Grissom II, Duc Minh Trinh

Modeling Localness for Self-Attention Networks (D18-1)

Challenge:	Recent work examines MT pathologies on classification problems, the worst being hallucinations.
Approach:	They propose a method to induce severe translation errors by deleting a single character . they also analyze the effect of training data size on the number and types of errors induced by these perturbations .
Outcome:	The proposed method induces severe errors in Chinese-English and English-Chinese translations.

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Baosong Yang, Zhaopeng Tu, Derek F. Wong, Fandong Meng, Lidia S. Chao, Tong Zhang

Semantic Text Processing

Challenge:	Existing approaches to model locality for self-attention networks have shown great value for capturing global dependencies.
Approach:	They propose to model localness for self-attention networks to capture local context . they cast localness modeling as a learnable Gaussian bias, which indicates the central and scope of the local region to be paid more attention.
Outcome:	The proposed model improves the ability to capture local context and improves accuracy.

AdvAug: Robust Adversarial Augmentation for Neural Machine Translation (2020.acl-main)

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Yong Cheng, Lu Jiang, Wolfgang Macherey, Jacob Eisenstein

Robustness in NLP

Challenge:	Recent work in neural machine translation has led to dramatic improvements in both research and commercial systems.
Approach:	They propose a adversarial augmentation method for Neural Machine Translation that minimizes vicinal risk over virtual sentences . they use a novel vicinity distribution for adversarials to describe a smooth interpolated embedding space .
Outcome:	The proposed method outperforms the current method on Chinese-English, English-French, and English-German translation benchmarks.

Learning Adaptive Segmentation Policy for End-to-End Simultaneous Translation (2022.acl-long)

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Ruiqing Zhang, Zhongjun He, Hua Wu, Haifeng Wang

Speech & Audio in NLP

Challenge:	Existing methods to perform simultaneous speech-to-text translation ignore contextual information and suffer from low translation quality.
Approach:	They propose an adaptive segmentation policy for simultaneous speech-to-text translation . it learns to segment the source streaming speech into meaningful units .
Outcome:	The proposed method achieves a good accuracy-latency trade-off over state-of-the-art methods on English-German and Chinese-English.

New Datasets and Controllable Iterative Data Augmentation Method for Code-switching ASR Error Correction (2023.findings-emnlp)

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Zhaohong Wan, Xiaojun Wan, Wei Peng, Rongjun Li

Multimodality

Challenge:	In bilingual or multilingual settings, code-switching ASR has greater challenges and research value.
Approach:	They propose a controllable iterative method for improving the performance of mainstream automatic speech recognition systems by using Chinese-English code-switching dialogues.
Outcome:	The proposed method achieves the best performance compared with the rule-based, back-translation-based data augmentation methods and large language model ChatGPT.

Document Graph for Neural Machine Translation (2021.emnlp-main)

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Mingzhou Xu, Liangyou Li, Derek F. Wong, Qun Liu, Lidia S. Chao

CUTE: A Multilingual Dataset for Enhancing Cross-Lingual Knowledge Transfer in Low-Resource Languages (2025.coling-main)

Challenge:	Existing document-level NMT methods fail to leverage contexts beyond a few set of previous sentences.
Approach:	They propose to represent a document as a graph that connects relevant contexts regardless of distances.
Outcome:	Experiments on IWSLT English–French, Chinese-English, WMT English–German and Opensubtitle English–Russian show that using document graphs can significantly improve translation quality.

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Wenhao Zhuang, Yuan Sun

FGraDA: A Dataset and Benchmark for Fine-Grained Domain Adaptation in Machine Translation (2022.lrec-1)

Challenge:	Existing multilingual models such as XLM-R support only approximately 100-200 languages, leaving nearly 7,000 low-resource languages untapped.
Approach:	They construct and open-source a dataset of four-language corpora obtained through machine translation into Chinese, Uyghur and Tibetan.
Outcome:	The proposed dataset includes two resource-rich languages and two low-resource languages.

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Wenhao Zhu, Shujian Huang, Tong Pu, Pingxuan Huang, Xu Zhang, Jian Yu, Wei Chen, Yanfeng Wang, Jiajun Chen

Humanistic Buddhism Corpus: A Challenging Domain-Specific Dataset of English Translations for Classical and Modern Chinese (2024.lrec-main)

Challenge:	Recent research on domain adaptation neglects diversity in translation within a domain . current research on NMT models considers very broad target domains .
Approach:	They propose a fine-grained domain adaptation task for autonomous vehicles, AI education, real-time networks, and smart phone.
Outcome:	The proposed task is compared with a dataset of Chinese-English translation tasks for four sub-domains of information technology: autonomous vehicles, AI education, real-time networks, and smart phone.

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Youheng W. Wong, Natalie Parde, Erdem Koyuncu

GuoFeng: A Benchmark for Zero Pronoun Recovery and Translation (2022.emnlp-main)

Challenge:	Compared to existing machine translation datasets, HBC presents unique challenges . classical and modern Chinese texts are often translated in distant languages .
Approach:	They propose a dataset containing 80,000 Chinese-English parallel phrases extracted and translated from publications in the domain of Buddhism.
Outcome:	The Humanistic Buddhism Corpus (HBC) contains 80,000 parallel Chinese-English phrases extracted and translated from publications in the domain of Buddhism.

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Mingzhou Xu, Longyue Wang, Derek F. Wong, Hongye Liu, Linfeng Song, Lidia S. Chao, Shuming Shi, Zhaopeng Tu

Leveraging GPT-4 for Automatic Translation Post-Editing (2023.findings-emnlp)

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.

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Vikas Raunak, Amr Sharaf, Yiren Wang, Hany Awadalla, Arul Menezes

“A Little is Enough”: Few-Shot Quality Estimation based Corpus Filtering improves Machine Translation (2023.findings-acl)

Challenge:	Neural Machine Translation models still require translation post-editing to rectify errors and enhance quality under critical settings.
Approach:	They use GPT-4 to automatically post-edit NMT outputs across several language pairs . they show that GPT4 is adept at translation post- editing, producing meaningful edits .
Outcome:	The proposed translation post-editor improves on state-of-the-art language models on English-Chinese, English-German, Chinese-English and German-English language pairs.

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Akshay Batheja, Pushpak Bhattacharyya

Challenges in Context-Aware Neural Machine Translation (2023.emnlp-main)

Challenge:	Quality Estimation (QE) is the task of evaluating the quality of a translation when reference translation is unavailable.
Approach:	They propose a Quality Estimation based Filtering approach to extract high-quality parallel data from the pseudo-parallel corpus.
Outcome:	The proposed approach improves the machine translation system performance by up to 1.8 BLEU points over the baseline model.

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Linghao Jin, Jacqueline He, Jonathan May, Xuezhe Ma