Papers with English-Chinese
Translate the Beauty in Songs: Jointly Learning to Align Melody and Translate Lyrics (2023.findings-emnlp)
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| Challenge: | Song translation requires both translation of lyrics and alignment of music notes . human translators of songs need to have a mastery of cultural traditions and the poetic usage of both source and target languages . |
| Approach: | They propose a model that can model lyric translation and lyrics-melody alignment . they use an encoder-decoder framework that can translate lyrics and determine number of aligned notes . |
| Outcome: | The proposed framework can translate lyrics and determine the number of aligned notes at each decoding step. |
WantWords: An Open-source Online Reverse Dictionary System (2020.emnlp-demos)
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| Challenge: | Existing reverse dictionary systems only support English reverse dictionary queries . a reverse dictionary can help people who can't remember a word from memory . |
| Approach: | They propose an online reverse dictionary system that outperforms other reverse dictionary systems . it supports Chinese and English-Chinese as well as Chinese-English cross-lingual reverse dictionary queries . |
| Outcome: | The proposed reverse dictionary outperforms other reverse dictionary systems on performance . it supports Chinese and English-Chinese as well as Chinese-English queries . |
CLUSE: Cross-Lingual Unsupervised Sense Embeddings (D18-1)
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| Challenge: | Existing models for learning bilingual sense embeddings that encode semantics are weak for recognizing multi-sense word representations . elucidation of word embeddables is difficult because they do not allow a word to have different meanings in different contexts. |
| Approach: | They propose a sense induction and representation learning model that learns bilingual sense embeddings that align well in the vector space. |
| Outcome: | The proposed model shows that the learned embeddings are aligned well in the vector space. |
Songs Across Borders: Singable and Controllable Neural Lyric Translation (2023.acl-long)
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| Challenge: | Obtaining singable lyric translations can facilitate the globalization of the music publishing industry . |
| Approach: | They formalize lyric translation into a constrained translation problem and instantiate them to an English-Chinese system. |
| Outcome: | The proposed model achieves 99.85%, 99.00%, and 95.52% on length accuracy, rhyme accuracy, and word boundary recall. |
Understanding and Detecting Hallucinations in Neural Machine Translation via Model Introspection (2023.tacl-1)
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| Challenge: | Neural sequence generation models produce outputs that are unrelated to the source text, and are potentially harmful, yet it remains unclear in what conditions they arise and how to mitigate their impact. |
| Approach: | They first identify internal model symptoms of hallucinations by analyzing the relative token contributions to the generation in contrastive hallucinous vs. non-hallucinated outputs generated via source perturbations. |
| Outcome: | The proposed detector outperforms both baseline models and strong classifiers on English-Chinese and German-English translation test beds. |
A Discriminative Neural Model for Cross-Lingual Word Alignment (D19-1)
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| Challenge: | a novel word alignment model for machine translation has been developed for a number of languages . explicit word-to-word alignments have largely been lost in neural MT systems . |
| Approach: | They propose a discriminative word alignment model which integrates into a Transformer-based machine translation model. |
| Outcome: | The proposed model performs better on Chinese and Arabic alignments than standard models. |
Transfer of Frames from English FrameNet to Construct Chinese FrameNet: A Bilingual Corpus-Based Approach (L18-1)
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| Challenge: | Current publicly available Chinese FrameNet has a relatively low coverage of frames and lexical units compared with other languages. |
| Approach: | They propose an automatic way to construct Chinese FrameNet using a sentence-aligned English-Chinese bilingual corpus. |
| Outcome: | The proposed resource can provide frame recommendations acceptable by annotators. |
Cross2StrA: Unpaired Cross-lingual Image Captioning with Cross-lingual Cross-modal Structure-pivoted Alignment (2023.acl-long)
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| Challenge: | Current captioning models are limited to the English language due to the largescale paired image-caption datasets. |
| Approach: | They propose to integrate the scene graph (SG) structures and the syntactic constituency trees into a captioner to improve captioning relevancy and fluency. |
| Outcome: | The proposed model improves captioning relevancy and fluency on English-Chinese transfers. |
A Variational Hierarchical Model for Neural Cross-Lingual Summarization (2022.acl-long)
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| Challenge: | Existing studies on cross-lingual summarization focus on pipeline methods or jointly training an end-to-end model through an auxiliary MT or MS objective. |
| Approach: | They propose a hierarchical model for the cross-lingual summarization task . the model is based on the conditional variational auto-encoder . |
| Outcome: | The proposed model generates better cross-lingual summaries than comparison models in the few-shot setting. |
MSCTD: A Multimodal Sentiment Chat Translation Dataset (2022.acl-long)
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| Challenge: | Multimodal machine translation and textual chat translation have received considerable attention . however, little research has been devoted to multimodal machine translator in conversations . |
| Approach: | They propose a task to generate more accurate translations with the help of dialogue history and visual context. |
| Outcome: | The proposed task can generate more accurate translations with the help of dialogue history and visual context. |
Cross-Lingual Leveled Reading Based on Language-Invariant Features (2021.findings-emnlp)
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| Challenge: | Leveled reading (LR) aims to automatically classify texts by the cognitive levels of readers. |
| Approach: | They propose to use adversarial training and cross-lingual pre-training methods to transfer LR knowledge from annotated data in resource-rich English to Chinese. |
| Outcome: | The proposed method captures language-invariant features between English and Chinese. |
Scheduled Multi-task Learning for Neural Chat Translation (2022.acl-long)
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| Challenge: | Existing methods to improve chat translation focus on modeling bilingual dialogue characteristics . however, there is insufficient data and simple joint training methods . |
| Approach: | They propose a scheduled multi-task learning framework to incorporate large-scale chat translation data into training by adding a second pre-training stage between the original pre- training and fine-tuning stages. |
| Outcome: | The proposed framework improves translation tasks on four language directions. |
Loss in Translation: Learning Bilingual Word Mapping with a Retrieval Criterion (D18-1)
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| Challenge: | Existing approaches to learn orthogonal matrix aligning bilingual lexicons are suboptimal . resulting models suffer from "hubness problem" because word vectors tend to be nearest neighbors of abnormally high number of other words. |
| Approach: | They propose a unified formulation that directly optimizes a retrieval criterion in an end-to-end fashion. |
| Outcome: | The proposed approach outperforms the state-of-the-art on word translation on standard benchmarks. |
Improving Neural Machine Translation by Multi-Knowledge Integration with Prompting (2023.findings-emnlp)
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| Challenge: | Existing methods focus on how to integrate multiple types of knowledge into NMT models . |
| Approach: | They propose a framework that integrates multiple types of knowledge into NMT models . they use multiple types as prefix-prompts of input for the encoder and decoder . |
| Outcome: | The proposed framework outperforms baselines on English-Chinese and English-German translation. |
Rare but Severe Neural Machine Translation Errors Induced by Minimal Deletion: An Empirical Study on Chinese and English (2022.coling-1)
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| 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. |
Improving Both Domain Robustness and Domain Adaptability in Machine Translation (2022.coling-1)
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| Challenge: | Existing approaches to domain adaptation for NMT depend on high-quality parallel data. |
| Approach: | They propose a meta-learning framework which improves domain robustness and adaptability . they use a word-level domain mixing model and a domain classifier to integrate it . |
| Outcome: | The proposed approach improves domain robustness and adaptability in seen and unseen domains. |
Building an English-Chinese Parallel Corpus Annotated with Sub-sentential Translation Techniques (2020.lrec-1)
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| Challenge: | a recent study shows that human translators often resort to different non-literal translation techniques besides literal translation . however, they receive less attention in developing natural language processing (NLP) applications. |
| Approach: | They propose to have a better semantic control of extracting paraphrases from bilingual parallel corpora. |
| Outcome: | The proposed method can automatically recognize different non-literal translation techniques . the results confirm the hypothesis of the proposed method . |
Weakly Supervised Cross-lingual Semantic Relation Classification via Knowledge Distillation (D19-1)
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| Challenge: | Existing work characterizes differences in meaning between words across languages using semantic relations . however, because of translation ambiguity, semantic relations are not always preserved by translation. |
| Approach: | They propose a cross-lingual relation classifier trained only with English examples and a bilingual dictionary to account for translation ambiguity when transferring knowledge from English to cross-linguistic settings. |
| Outcome: | The proposed model outperforms baselines that rely on bilingual embeddings or dictionaries for cross-lingual transfer and approaches fully supervised systems on English tasks. |
NegPar: A parallel corpus annotated for negation (L18-1)
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| Challenge: | NegPar is the first parallel corpus annotated for negation in the narrative domain. |
| Approach: | They present NegPar, a parallel corpus annotated for negation in the narrative domain . they follow the annotation guidelines in the CONANDOYLE-NEG corpus . |
| Outcome: | The proposed corpus is based on the CONANDOYLE-NEG corpus and is reannotated to ensure more consistent and interpretable representations. |
Redefining Machine Simultaneous Interpretation: From Incremental Translation to Human-Like Strategies (2026.findings-acl)
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| Challenge: | Simultaneous machine translation requires high-quality translations under strict real-time constraints. |
| Approach: | They extend the action space of simultaneous machine translation with four adaptive actions . they adapt these actions in a large language model framework and construct training references . |
| Outcome: | The proposed framework improves semantic metrics and achieves lower delay compared to reference translations and salami-based baselines. |
FinSafetyBench: Evaluating LLM Safety in Real-World Financial Scenarios (2026.findings-acl)
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| Challenge: | Existing large language models (LLMs) are prone to misuse and misinformation, posing serious compliance risks. |
| Approach: | They propose a bilingual red-teaming benchmark to test an LLM’s refusal of requests that violate financial compliance. |
| Outcome: | The proposed benchmark is based on real-world financial crime cases and ethical violations and includes 14 subcategories covering financial crimes and ethical breaches. |
Grammatical Error Correction for Code-Switched Sentences by Learners of English (2024.lrec-main)
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| Challenge: | Existing grammar error correction systems have been trained on monolingual data and not developed for CSW text. |
| Approach: | They propose a method of generating synthetic CSW GEC datasets by translating different spans of text within existing GEC corpora and investigate different methods of selecting these spans based on CSW ratio, switch-point factor and linguistic constraints. |
| Outcome: | The proposed model achieves an average increase of 1.57 F0.5 across 3 CSW test sets (English-Chinese, English-Korean and English-Japanese) without affecting the model’s performance on a monolingual dataset. |
What is the Best Way for ChatGPT to Translate Poetry? (2024.acl-long)
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| Challenge: | Despite promising results, our analysis reveals persistent issues in the translations generated by ChatGPT that warrant attention. |
| Approach: | They propose an Explanation-Assisted Poetry Machine Translation method which leverages monolingual poetry explanation as a guiding information for the translation process. |
| Outcome: | The proposed method outperforms traditional translation methods of ChatGPT and the existing online systems in English-Chinese poetry translation. |
Generative Error Correction for Emotion-aware Speech-to-text Translation (2025.findings-acl)
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| Challenge: | Despite recent advances in speech-to-text translation, the impact of the emotion content has been overlooked. |
| Approach: | They propose to use generative error correction (GER) to generate the translation based on the decoded N-best hypotheses and combine emotion and sentiment labels into the LLM finetuning process to enable the model to consider the emotion content. |
| Outcome: | The proposed model can translate speech in English-Chinese using GER and emotion and sentiment labels. |