Papers by Xiaohui Yan
ESPnet-ST-v2: Multipurpose Spoken Language Translation Toolkit (2023.acl-demo)
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Brian Yan, Jiatong Shi, Yun Tang, Hirofumi Inaguma, Yifan Peng, Siddharth Dalmia, Peter Polák, Patrick Fernandes, Dan Berrebbi, Tomoki Hayashi, Xiaohui Zhang, Zhaoheng Ni, Moto Hira, Soumi Maiti, Juan Pino, Shinji Watanabe
| Challenge: | ESPnet-ST-v2 is a revamp of the open-source spoken language translation toolkit . it supports offline speech-to-text translation (ST), simultaneous speech- to-text (SST), and offline speech to-speech (S2ST) |
| Approach: | They propose to revamp the open-source ESPnet-ST toolkit to support offline speech-to-text translation, simultaneous speech- to-text and offline speech to-speech translation. |
| Outcome: | The updated version of ESPnet-ST supports offline speech-to-text translation (ST), simultaneous speech- to-text (SST), and offline speech to-speech translation (S2ST). |
Abstract Meaning Representation for Paraphrase Detection (N18-1)
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| Challenge: | Abstract Meaning Representation (AMR) parsing is ideal for paraphrase detection . it abstracts away from the syntactic realization of a sentence, and denotes only its meaning in a canonical form. |
| Approach: | They propose a technique that uses latent semantic analysis to translate sentences into AMR graphs . they show that the technique can be used to detect whether two sentences have the same meaning . |
| Outcome: | The proposed technique significantly advances state-of-the-art paraphrase detection for the Microsoft Research Paraphrase Corpus. |
Knowledge-Augmented Question Error Correction for Chinese Question Answer System with QuestionRAG (2025.emnlp-industry)
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| Challenge: | Large language models struggle with input errors, often failing to interpret user intent or altering the original question’s structure (over-correction). |
| Approach: | They propose a framework that uses reinforcement learning to address misinterpretation and over-correction by integrating external knowledge with the input. |
| Outcome: | The proposed framework unlocks the full potential of LLMs for the question correction task. |
Learning Representation Mapping for Relation Detection in Knowledge Base Question Answering (P19-1)
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| Challenge: | Existing approaches to detect relation detection only get high accuracy for questions whose relations have been seen in training data. |
| Approach: | They propose a method to learn representation mapping for both seen and unseen relations based on previously learned relation embedding. |
| Outcome: | The proposed method improves the performance of unseen relations while keeping the performance comparable to the state-of-the-art. |
CC-Tuning: A Cross-Lingual Connection Mechanism for Improving Joint Multilingual Supervised Fine-Tuning (2025.acl-long)
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Yangfan Ye, Xiaocheng Feng, Zekun Yuan, Xiachong Feng, Libo Qin, Lei Huang, Weitao Ma, Yichong Huang, Zhirui Zhang, Yunfei Lu, Xiaohui Yan, Duyu Tang, Dandan Tu, Bing Qin
| Challenge: | Existing fine-tuning approaches that focus on English-centric training corpora often introduce implicit cross-lingual alignment, overlooking the potential for more profound, latent-level cross-linguistic interactions. |
| Approach: | They propose a multilingual fine-tuning paradigm that explicitly establishes a cross-lingual connection mechanism at the latent level. |
| Outcome: | The proposed model outperforms vanilla SFT and offers a strong latent-level alternative to data-level augmentation methods. |
Improve LLM-as-a-Judge Ability as a General Ability (2025.emnlp-main)
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| Challenge: | Recent studies focus on generative judges, but only on their judge ability. |
| Approach: | They propose a method that leverages the generative and reasoning capabilities of large language models to evaluate LLM responses across diverse scenarios, providing accurate preference signals. |
| Outcome: | The proposed model performs on RewardBench with only 2% to 40% of the data required by other training frameworks. |
Zero-shot User Intent Detection via Capsule Neural Networks (D18-1)
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| Challenge: | Existing methods to classify intents are labor-intensive and time-consuming as intents will be diverse and new intents may be involved. |
| Approach: | They propose a zero-shot intent detection problem which aims to detect emerging user intents where no labeled utterances are currently available. |
| Outcome: | The proposed model can discriminate emerging intents when no labeled utterances are available in training data. |
Misleading Relation Classifiers by Substituting Words in Texts (2023.findings-acl)
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| Challenge: | Existing methods to generate adversarial examples for relation classification are vulnerable to adversarials. |
| Approach: | They propose a method that uses most important parts of speech to substitute words with synonyms or hyponyms to generate adversarial texts of high quality. |
| Outcome: | The proposed method can generate adversarial texts of high quality and most relationships can be correctly identified in the process of human evaluation. |