Papers by Kai Fan

wav2vec-S: Adapting Pre-trained Speech Models for Streaming (2024.findings-acl)

Challenge:	Sign language is an effective non-verbal communication mode for the hearingimpaired people.
Approach:	They propose a three-form scheme to represent dynamic CSL gestures using a word-based dataset.
Outcome:	The proposed framework integrates the local sequential sensor data derived from the wearable-based CSL gestures with the global, fine-grained skeleton representations captured from video-based gestures simultaneously.

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Biao Fu, Kai Fan, Minpeng Liao, Yidong Chen, Xiaodong Shi, Zhongqiang Huang

LLMs Can Achieve High-quality Simultaneous Machine Translation as Efficiently as Offline (2025.findings-acl)

Challenge:	Pre-trained speech models have advanced speech-related tasks, including speech recognition and translation.
Approach:	They propose a pre-trained speech model that incorporates modifications to ensure consistent speech representations during training and inference phases for streaming speech inputs.
Outcome:	The proposed model outperforms baseline models on speech recognition and translation tasks and achieves a superior balance between quality and latency.

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Biao Fu, Minpeng Liao, Kai Fan, Chengxi Li, Liang Zhang, Yidong Chen, Xiaodong Shi

Responsible & Trustworthy NLP

Challenge:	Large language models perform well in offline machine translation when the complete source sentence is provided . however, in many real scenarios, the source tokens arrive in a streaming manner and simultaneous machine translation is required .
Approach:	They propose a new paradigm that includes constructing supervised fine-tuning data for simultaneous machine translation (SiMT) to achieve SiMT, source and target tokens are rearranged into interleaved sequences, separated by special tokens according to varying latency requirements.
Outcome:	The proposed approach achieves state-of-the-art performance across various SiMT benchmarks and evaluation metrics while maintaining efficient auto-regressive decoding.

MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)

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Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He, Fan Wu, Qintong Zhang, Zhenjiang Jin, Guang Liang, Rui Zhang, Wenzheng Zhang, Yuan Qu, Zhifei Ren, Yuefeng Sun, Zirui Tang, Boyu Niu, Yuanhong Zheng, Dongsheng Ma, Ziyang Miao, Hejun Dong, Siyi Qian, Junyuan Zhang, Fangdong Wang, Jingzhou Chen, Xiaomeng Zhao, Liqun Wei, Wei Li, Shasha Wang, RuiLiang Xu, Yuanyuan Cao, Lu Chen, Qianqian Wu, Huaiyu Gu, Lindong Lu, Dechen Lin, null Shenguanlin, Xuanhe Zhou, Linfeng Zhang, Yuhang Zang, Xiaoyi Dong, Jiaqi Wang, Bo Zhang, Lei Bai, Pei Chu, Weijia Li, Jiang Wu, Lijun Wu, Zhenxiang Li, Guangyu Wang, Zhongying Tu, Chao Xu, Kai Chen, Bowen Zhou, Dahua Lin, Wentao Zhang, Conghui He

Green & Sustainable NLP

Challenge:	Document images are characterized by higher resolutions, denser content, and more complex structural layouts.
Approach:	They propose a 1.2B-parameter document parsing vision-language model that decouples layout analysis from local content recognition.
Outcome:	The proposed model surpasses general-purpose and domain-specific models on multiple benchmarks while maintaining significantly lower computational overhead.

Computer Assisted Translation with Neural Quality Estimation and Automatic Post-Editing (2020.findings-emnlp)

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Ke Wang, Jiayi Wang, Niyu Ge, Yangbin Shi, Yu Zhao, Kai Fan

Machine Translation

Challenge:	Using neural machine translation to approximate human parity is difficult due to the lack of parallel training corpora.
Approach:	They propose an end-to-end deep learning framework for quality estimation and automatic post-editing of machine translation output.
Outcome:	The proposed framework achieves state-of-the-art performance on the English–German dataset and human translators can significantly expedite their post-editing processing with the model.

LLM-REDIAL: A Large-Scale Dataset for Conversational Recommender Systems Created from User Behaviors with LLMs (2024.findings-acl)

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Tingting Liang, Chenxin Jin, Lingzhi Wang, Wenqi Fan, Congying Xia, Kai Chen, Yuyu Yin

Dialogue Systems & Conversational Agents

Challenge:	Existing CRS datasets suffer from data inextensibility and semantic inconsistency .
Approach:	They introduce the LLM-REDIAL dataset to facilitate the research in CRS by leveraging large language models to generate high-quality dialogues.
Outcome:	The proposed dataset is the largest multi-domain CRS dataset which consists of 47.6k multi-turn dialogues with 482.6k utterances across 4 domains.

Better Simultaneous Translation with Monotonic Knowledge Distillation (2023.acl-long)

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Shushu Wang, Jing Wu, Kai Fan, Wei Luo, Jun Xiao, Zhongqiang Huang

Sparsity-Accelerated Training for Large Language Models (2024.findings-acl)

Challenge:	Existing methods to train offline MT models require generating target tokens before source sentence is fully consumed.
Approach:	They propose a method that leverages traditional translation models as teachers to generate monotonic yet accurate reference translations for sequence-level knowledge distillation.
Outcome:	The proposed approach improves on strong baselines and on a monotonic version of the WMT15 De-En test set.

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Da Ma, Lu Chen, Pengyu Wang, Hongshen Xu, Hanqi Li, Liangtai Sun, Su Zhu, Shuai Fan, Kai Yu

Towards Zero-shot Learning for End-to-end Cross-modal Translation Models (2023.findings-emnlp)

Challenge:	Large language models (LLMs) have demonstrated proficiency across various NLP tasks but often require additional training, such as continual pre-training and supervised fine-tuning.
Approach:	They propose to leverage sparsity in pre-trained LLMs to accelerate training by disregarding computations for unimportant neurons.
Outcome:	The proposed framework achieves comparable or superior performance to standard training while significantly accelerating the process.

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Jichen Yang, Kai Fan, Minpeng Liao, Boxing Chen, Zhongqiang Huang

Alignment for Efficient Tool Calling of Large Language Models (2025.emnlp-main)

Challenge:	End-to-end zero-shot speech translation model is based on a zero-shot approach, but it is less competitive because of the limited amount of data available for multiple modalities.
Approach:	They propose an end-to-end zero-shot speech translation model that connects two pre-trained uni-modality modules via word rotator’s distance.
Outcome:	The proposed model performs better than or as well as those of the CTC-based models and can be trained in an end-to-end style to avoid error propagation.

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Hongshen Xu, Zihan Wang, Zichen Zhu, Lei Pan, Xingyu Chen, Shuai Fan, Lu Chen, Kai Yu

Semantic Text Processing

Challenge:	Recent advances in tool learning have enabled large language models to integrate external tools, enhancing their task performance by expanding their knowledge boundaries.
Approach:	They propose a framework that combines probabilistic knowledge boundary estimation with dynamic decision-making to allow LLMs to better assess when to invoke tools based on their confidence.
Outcome:	The proposed framework shows significant improvements in tool efficiency by reducing unnecessary tool usage.

Lattice Transformer for Speech Translation (P19-1)

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Pei Zhang, Niyu Ge, Boxing Chen, Kai Fan

ARM2: Adaptive Reasoning Model with Vision Understanding and Executable Code (2026.findings-acl)

Challenge:	Recent advances in sequence modeling have highlighted the strengths of the transformer architecture.
Approach:	They propose a general lattice transformer for speech translation where the input is the output of the automatic speech recognition (ASR) they propose 'controllable' lattica attention mechanism to consume latent representations.
Outcome:	The proposed model outperforms baseline and lattice LSTM on the Chinese-English translation task.

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Jian Xie, Zhendong Chu, Aoxiao Zhong, Kai Zhang, Mingzhe Han, Xing Fan, Jialie Shen, Qingsong Wen

Fixing Distribution Shifts of LLM Self-Critique via On-Policy Self-Play Training (2025.acl-long)

Challenge:	Large Reasoning Models suffer from the "over-thinking" problem, causing performance degradation.
Approach:	They propose a unified model that balances reasoning performance and efficiency across multiple formats through a reinforcement learning framework augmented with length-aware optimization.
Outcome:	The proposed model reduces token costs while preserving performance compared to traditional models.

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Rong Bao, Donglei Yu, Kai Fan, Minpeng Liao

AnyTrans: Translate AnyText in the Image with Large Scale Models (2024.findings-emnlp)

Challenge:	Large language models show impressive performance in a wide range of linguistic tasks, but their performance on complex reasoning tasks is still signif-icantly lower than the human level.
Approach:	They propose a reinforcement learning framework to synchronize the reasoning and critique capabilities of language models by using Monte Carlo sampling to give appropriate rewards to the model's critique content.
Outcome:	The proposed framework improves the model's reasoning and critique capabilities by 5.40 and 3.66 points, respectively, compared to the best baseline approach.

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Zhipeng Qian, Pei Zhang, Baosong Yang, Kai Fan, Yiwei Ma, Derek Wong, Xiaoshuai Sun, Rongrong Ji

Multilinguality

Challenge:	Recent advances in natural language processing and computer vision have made it possible to translate images with text in one language into equivalent images displaying that text translated into another language.
Approach:	They propose an all-encompassing framework for the task–In-Image Machine Translation (IIMT) that incorporates contextual cues from both textual and visual elements during translation.
Outcome:	The proposed framework can be constructed using open-source models and requires no training, making it highly accessible and expandable.

MARIO: MAth Reasoning with code Interpreter Output - A Reproducible Pipeline (2024.findings-acl)

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Minpeng Liao, Chengxi Li, Wei Luo, Wu Jing, Kai Fan

Responsible & Trustworthy NLP

Challenge:	Large language models lack mathematical reasoning, a hurdle on the path to true artificial general intelligence.
Approach:	They propose a protocol for fine-tuning large language models with a Python code interpreter to enhance the text analysis of the LLMs.
Outcome:	The proposed protocol improves the performance of a 7B-parameter LLM on the GSM8K and MATH datasets while allowing for an outlier-free value model-based inference method.

Translate the Beauty in Songs: Jointly Learning to Align Melody and Translate Lyrics (2023.findings-emnlp)

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Chengxi Li, Kai Fan, Jiajun Bu, Boxing Chen, Zhongqiang Huang, Zhi Yu

Machine Translation

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.

X-LeBench: A Benchmark for Extremely Long Egocentric Video Understanding (2025.findings-emnlp)

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Wenqi Zhou, Kai Cao, Hao Zheng, Yunze Liu, Xinyi Zheng, Miao Liu, Per Ola Kristensson, Walterio W. Mayol-Cuevas, Fan Zhang, Weizhe Lin, Junxiao Shen

Towards Transferable Personality Representation Learning based on Triplet Comparisons and Its Applications (2025.emnlp-main)

Challenge:	Existing benchmark datasets focus on short to moderately long videos, leaving a substantial gap in evaluating extensive, ultra-long egocentric video recordings.
Approach:	X-LeBench is a benchmark dataset designed to evaluate long egocentric video recordings . it uses a life-logging pipeline to produce realistic, coherent daily plans .
Outcome:	X-LeBench is a new benchmark dataset designed to evaluate long-form egocentric video understanding . the approach produces realistic, coherent daily plans aligned with real-world video data .

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Kai Tang, Rui Wang, Renyu Zhu, Minmin Lin, Xiao Ding, Tangjie Lv, Changjie Fan, Runze Wu, Haobo Wang

Representation Learning

Challenge:	Existing methods for personality analysis treat corpus as a single unit for classification, but this approach presents several challenges.
Approach:	They propose a task paradigm for text-based personality representation learning that uses a triplet personality trend comparison dataset to learn single-sentence personality embeddings with desirable metric properties.
Outcome:	The proposed model significantly boosts performance across various applications, including personality detection, personality retrieval, and emotion translation prediction.

CAMIEval: Enhancing NLG Evaluation through Multidimensional Comparative Instruction-Following Analysis (2025.naacl-long)

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Ziyue Fan, Junliang He, Li Xiaoqing, Shaohui Kuang, Kai Song, Yaqian Zhou, Xipeng Qiu

Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers? (2025.coling-main)

Challenge:	Evaluating the quality of texts generated by language models has always been a challenging task in natural language processing (NLP).
Approach:	They propose a multidimensional comparative evaluation method based on instruction-following that combines relevance, factuality, and adherence with a concrete Chain-of-Thoughts process to enhance the accuracy of evaluations.
Outcome:	The proposed method outperforms existing methods in correlation with human evaluations on two NLG evaluation benchmarks.

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Mingyu Jin, Qinkai Yu, Jingyuan Huang, Qingcheng Zeng, Zhenting Wang, Wenyue Hua, Haiyan Zhao, Kai Mei, Yanda Meng, Kaize Ding, Fan Yang, Mengnan Du, Yongfeng Zhang

VideoScore: Building Automatic Metrics to Simulate Fine-grained Human Feedback for Video Generation (2024.emnlp-main)

Challenge:	Large language models have shown remarkable performances across a wide range of tasks, but mechanisms by which they encode tasks of varying complexity remain poorly understood.
Approach:	They propose to explore the possibility that LLMs process concepts in different layers . they propose to categorize concepts based on their level of abstraction .
Outcome:	The proposed model can process complex concepts in shallow layers, the authors show . the proposed model could be used to prob complex tasks in shallow ones .

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Xuan He, Dongfu Jiang, Ge Zhang, Max Ku, Achint Soni, Sherman Siu, Haonan Chen, Abhranil Chandra, Ziyan Jiang, Aaran Arulraj, Kai Wang, Quy Do, Yuansheng Ni, Bohan Lyu, Yaswanth Narsupalli, Rongqi Fan, Zhiheng Lyu, Bill Yuchen Lin, Wenhu Chen

Visual Data in NLP

Challenge:	Existing video metrics are lagging behind in providing reliable scores over generated videos due to lack of large-scale human-annotated dataset.
Approach:	They propose to use VideoFeedback to train a human-annotated multi-aspect score over 37.6K synthesized videos from 11 existing video generative models.
Outcome:	The proposed model outperforms the prior best metrics by 50 points in the test.

NeuSym-RAG: Hybrid Neural Symbolic Retrieval with Multiview Structuring for PDF Question Answering (2025.acl-long)

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Ruisheng Cao, Hanchong Zhang, Tiancheng Huang, Zhangyi Kang, Yuxin Zhang, Liangtai Sun, Hanqi Li, Yuxun Miao, Shuai Fan, Lu Chen, Kai Yu

Natural Language Interfaces

Challenge:	Existing approaches to retrieval augmented generation neglect PDF structure and layout . individual PDFs often exceed prompt limits and user queries may span multiple documents.
Approach:	They propose a hybrid neural symbolic retrieval framework which combines both paradigms in an interactive process.
Outcome:	The proposed framework organizes semi-structured PDF content into relational database and vectorstore . it defeats both RAG and structured baselines on three PDF-based QA datasets .

GigaSpeech 2: An Evolving, Large-Scale and Multi-domain ASR Corpus for Low-Resource Languages with Automated Crawling, Transcription and Refinement (2025.acl-long)

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Yifan Yang, Zheshu Song, Jianheng Zhuo, Mingyu Cui, Jinpeng Li, Bo Yang, Yexing Du, Ziyang Ma, Xunying Liu, Ziyuan Wang, Ke Li, Shuai Fan, Kai Yu, Wei-Qiang Zhang, Guoguo Chen, Xie Chen

Improve Speech Translation Through Text Rewrite (2025.coling-industry)

Challenge:	GigaSpeech 2 is a large-scale, multi-domain, multilingual speech recognition corpus for low-resource languages.
Approach:	They propose a large-scale, multi-domain, multilingual speech recognition corpus for low-resource languages and an automated pipeline for data crawling, transcription, and label refinement.
Outcome:	The proposed corpus reduces the word error rate for Thai, Indonesian, and Vietnamese on a realistic YouTube test set by 25% to 40% compared to Whisper large-v3.

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Jing Wu, Shushu Wang, Kai Fan, Wei Luo, Minpeng Liao, Zhongqiang Huang

Probing Multi-modal Machine Translation with Pre-trained Language Model (2021.findings-acl)

Challenge:	Recent advances in speech translation (ST) research have focused on the unique characteristics of spontaneous speech, including accents and presentation quality.
Approach:	They propose to transform transcribed speech into a cleaner style more in line with the expectations of translation models built from written text.
Outcome:	Experiments on public and in-house translation models show that the proposed model can be effectively distilled into a standalone translation model.

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Kong Yawei, Kai Fan

Structure-aware Domain Knowledge Injection for Large Language Models (2025.acl-long)

Challenge:	Multi-modal machine translation (MMT) aimed at using images to help disambiguate the target during translation but recent studies showed that visual features are either negligible or incremental.
Approach:	They propose to incorporate a visual language model on the source side to improve multi-modal translation quality significantly.
Outcome:	The proposed model improves the translation quality significantly on the multi-modal dataset.

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Kai Liu, Ze Chen, Zhihang Fu, Wei Zhang, Rongxin Jiang, Fan Zhou, Yaowu Chen, Yue Wu, Jieping Ye

Semantic Text Processing

Challenge:	Structure-aware Continual Pre-Training (SCPT) and Structure-Aware Supervised Fine-Tuning (SSFT) are two-stage strategies for knowledge injection and alignment that reduces the training corpus needs to 5% while achieving 100% of traditional knowledge injection performance.
Approach:	They propose a method to efficiently transform foundation Large Language Models into domain specialists by using two-stage strategies: Structure-aware Continual Pre-Training and Structure-Aware Supervised Fine-Tuning.
Outcome:	The proposed method significantly reduces the training corpus needs to a mere 5% while achieving 100% of traditional knowledge injection performance.

Long-Short Term Masking Transformer: A Simple but Effective Baseline for Document-level Neural Machine Translation (2020.emnlp-main)

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Pei Zhang, Boxing Chen, Niyu Ge, Kai Fan

Structural Supervision for Word Alignment and Machine Translation (2022.findings-acl)

Challenge:	Recent advances in deep learning have led to significant improvement of document-level neural machine translation (NMT).
Approach:	They propose a long-short term masking self-attention on top of the standard transformer to capture the long-range dependence and reduce the propagation of errors.
Outcome:	The proposed model captures the long-range dependence and reduces errors on two publicly available document-level datasets.

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Lei Li, Kai Fan, Hongjia Li, Chun Yuan

Multilinguality

Challenge:	Existing knowledge on syntactic structure neglects the rich structural information from target tokens and the structural similarity between the source and target sentences.
Approach:	They propose to incorporate syntactic structure of both source and target tokens into the encoder-decoder framework, tightly correlating the internal logic of word alignment and machine translation for multi-task learning.
Outcome:	The proposed method outperforms baselines on four publicly available language pairs and consistently outperformed baselines in alignment accuracy and translation quality.

Adapting Offline Speech Translation Models for Streaming with Future-Aware Distillation and Inference (2023.emnlp-main)

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Biao Fu, Minpeng Liao, Kai Fan, Zhongqiang Huang, Boxing Chen, Yidong Chen, Xiaodong Shi

Knowledge as A Bridge: Improving Cross-domain Answer Selection with External Knowledge (C18-1)

Challenge:	Existing approaches to streaming speech translation use an offline model with a wait-k policy . however, there is a mismatch problem with an offline inference model trained with complete utterances .
Approach:	They propose an offline streaming speech translation model with wait-k policy to support different latency requirements.
Outcome:	The proposed model achieves better trade-offs between translation quality and latency than baselines.

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Yang Deng, Ying Shen, Min Yang, Yaliang Li, Nan Du, Wei Fan, Kai Lei

Question Answering

Challenge:	Existing approaches to answer selection are limited in domains with limited labeled data.
Approach:	They propose a Knowledge-aware Attentive Network framework for cross-domain answer selection that uses the knowledge base as a bridge to enable knowledge transfer from the source domain to the target domain.
Outcome:	The proposed model outperforms strong competitors by a noticeable margin in cross-domain answer selection.

FiNE: Filtering and Improving Noisy Data Elaborately with Large Language Models (2025.naacl-long)

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Junliang He, Ziyue Fan, Shaohui Kuang, Li Xiaoqing, Kai Song, Yaqian Zhou, Xipeng Qiu

Training Simultaneous Speech Translation with Robust and Random Wait-k-Tokens Strategy (2023.emnlp-main)

Challenge:	Currently, there are two mainstream methods for improving data integrity: data filtering and data augmentation.
Approach:	They propose a method to improve data integrity by combining data filtering and data augmentation with LLMs.
Outcome:	The proposed method surpasses the open-source chat version on HalluQA by 8.45 on the open source version.

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Linlin Zhang, Kai Fan, Jiajun Bu, Zhongqiang Huang

Efficient Cluster-Based k-Nearest-Neighbor Machine Translation (2022.acl-long)

Challenge:	Simultaneous Speech Translation (SimulST) is a task focused on ensuring high-quality translation of speech in low-latency situations.
Approach:	They propose a token-level cross-modal alignment method to improve the translation of text to audio . they use audio transcription pairs to pre-train the encoder and a random wait-k-tokens strategy to optimize the task.
Outcome:	The proposed method achieves better trade-off between translation quality and latency.

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Dexin Wang, Kai Fan, Boxing Chen, Deyi Xiong

FedMKT: Federated Mutual Knowledge Transfer for Large and Small Language Models (2025.coling-main)

Challenge:	k-Nearest-Neighbor Machine Translation (kNN-MT) is a non-parametric solution for domain adaptation . previous studies have shown that kNN retrieval is at the expense of high latency .
Approach:	They propose to use clustering to improve retrieval efficiency by combining a non-parametric MT with an in-domain feature-based retrieval module.
Outcome:	The proposed method reduces translation latency by 57% while maintaining the most useful information of the original datastore.

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Tao Fan, Guoqiang Ma, Yan Kang, Hanlin Gu, Yuanfeng Song, Lixin Fan, Kai Chen, Qiang Yang

Semantic Text Processing

Challenge:	Recent research in large language models (LLMs) has focused on enabling clients to fine-tune their locally deployed homogeneous LLMs collaboratively or on transferring knowledge from server-based LLM to small language models at downstream clients.
Approach:	They propose a parameter-efficient federated mutual knowledge transfer framework for large and small language models that allows for token alignment and selective knowledge transfer between client-side LLMs and a server-side SLM.
Outcome:	The proposed framework enhances the performance of both LLMs and SLMs with clients' unique domain insights while preserving the server's LLM and client's unique domain insight.

From Data-Centric to Sample-Centric: Enhancing LLM Reasoning via Progressive Optimization (2026.acl-long)

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Xinjie Chen, Minpeng Liao, Guoxin Chen, Chengxi Li, Biao Fu, Kai Fan, Xinggao Liu

Reasoning

Challenge:	Reinforcement learning with verifiable rewards (RLVR) has recently advanced the reasoning capabilities of large language models (LLMs).
Approach:	They propose a method that incorporates partial solution prefixes from expert demonstrations to guide the policy.
Outcome:	The proposed methods outperform strong baselines, yielding faster convergence and a higher performance ceiling.

FedProxy: Federated Fine-Tuning of LLMs via Proxy SLMs and Heterogeneity-Aware Fusion (2026.acl-long)

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Tao Fan, Guoqiang Ma, Yuanfeng Song, Lixin Fan, Kai Chen, Qiang Yang

Cooperative Denoising for Distantly Supervised Relation Extraction (C18-1)

Challenge:	Existing methods for fine-tuning Large Language Models (LLMs) suffer from a performance bottleneck . Existing approaches like Offsite-Tuning (OT) secure the LLMs IP .
Approach:	They propose a framework that replaces weak adapters with a unified, powerful Proxy Small Language Model (SLM) they propose 'resource-friendly' compression and 'robust optimization' to handle data heterogeneity.
Outcome:	Experiments show that FedProxy outperforms OT and centralized fine-tuning methods.

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Kai Lei, Daoyuan Chen, Yaliang Li, Nan Du, Min Yang, Wei Fan, Ying Shen

Information Extraction & Text Mining

Challenge:	Existing methods for distantly supervised relation extraction suffer from noisy labeling problem, which can severely degrade its performance.
Approach:	They propose a framework for distantly supervised relation extraction that leverages text corpus and knowledge graph and a cooperative module involving their mutual learning.
Outcome:	The proposed method reduces the noisy labels and achieves substantial improvement over the state-of-the-art methods.

A Simple Concatenation can Effectively Improve Speech Translation (2023.acl-short)

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Linlin Zhang, Kai Fan, Boxing Chen, Luo Si

Manifold Adversarial Augmentation for Neural Machine Translation (2021.findings-acl)

Challenge:	Experimental results show that in our unified cross-modal ST model, models can effectively utilize the auxiliary information from speech and text.
Approach:	They propose a unified cross-modal ST method which concatenates speech and text as the input and builds a teacher that can utilize both cross-modities simultaneously.
Outcome:	The proposed method can effectively utilize the auxiliary information from speech and text, and achieve compelling results on MuST-C datasets.

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Guandan Chen, Kai Fan, Kaibo Zhang, Boxing Chen, Zhongqiang Huang