Papers by Jie Chi
Improving Code-switched ASR with Linguistic Information (2022.coling-1)
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| Challenge: | Existing studies on code-switching have been limited to the individual languages, but the results are promising. |
| Approach: | They propose to apply linguistic theories to generate more realistic code-switching text, which is needed for language modelling in ASR. |
| Outcome: | The proposed system improves 2% on English-Spanish code-switching . Equivalence Constraint theory and part-of-speech labelling are particularly helpful for text generation and bring 2% improvement to ASR performance. |
Analyzing the Role of Part-of-Speech in Code-Switching: A Corpus-Based Study (2024.findings-eacl)
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| Challenge: | Code-switching (CS) is a common linguistic phenomenon wherein speakers fluidly transition between languages in conversation. |
| Approach: | They propose to use a part-of-speech (POS)-based analysis of Spanish-English and Mandarin-English corpora to examine the propensity of bilinguals to engage in CS. |
| Outcome: | The findings confirm the existence of a statistically significant connection between POS and the likelihood of CS across language pairs, but show that it diminishes as tokens distance themselves from CS instances. |
LaMP-Val: Large Language Models Empower Personalized Valuation in Auction (2025.findings-emnlp)
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Jie Sun, Tianyu Zhang, Houcheng Jiang, Kexin Huang, Xiang Shu, Zhibo Zhu, Lintao Ma, Xingyu Lu, Jun Zhou, Junkang Wu, Chi Luo, An Zhang, Jiancan Wu, Xiang Wang
| Challenge: | Currently, most research focuses on the bidding algorithms used within auction mechanisms. |
| Approach: | They propose a personalized valuation framework that integrates Large Language Models to incorporate personalized semantic preference into users valuation process. |
| Outcome: | The proposed framework incorporates Large Language Models to incorporate personalized semantic preference into users valuation process. |
The Role of Prosody in Spoken Question Answering (2025.findings-naacl)
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| Challenge: | lexical information is not available in most models, but prosody is important in understanding spoken language. |
| Approach: | They investigate the role of prosody in the process of answering a spoken question by isolating prosodic and lexical information from a natural speech dataset. |
| Outcome: | The proposed models can perform reasonably well on the SLUE-SQA-5 dataset, but when lexical information is available, models tend to predominantly rely on it. |
Discriminating Form and Meaning in Multilingual Models with Minimal-Pair ABX Tasks (2025.emnlp-main)
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| Challenge: | Existing studies have shown that multilingual models encode languagespecific information and language-agnostic features, but the nature and interaction of these representations is not fully understood. |
| Approach: | They propose a set of training-free ABX-style discrimination tasks to evaluate how multilingual language models represent language identity (form) and semantic content (meaning). |
| Outcome: | The proposed tasks show that language discrimination declines over training and strengthens over time and stabilizes in deeper layers. |
ChatMusician: Understanding and Generating Music Intrinsically with LLM (2024.findings-acl)
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Ruibin Yuan, Hanfeng Lin, Yi Wang, Zeyue Tian, Shangda Wu, Tianhao Shen, Ge Zhang, Yuhang Wu, Cong Liu, Ziya Zhou, Liumeng Xue, Ziyang Ma, Qin Liu, Tianyu Zheng, Yizhi Li, Yinghao Ma, Yiming Liang, Xiaowei Chi, Ruibo Liu, Zili Wang, Chenghua Lin, Qifeng Liu, Tao Jiang, Wenhao Huang, Wenhu Chen, Jie Fu, Emmanouil Benetos, Gus Xia, Roger Dannenberg, Wei Xue, Shiyin Kang, Yike Guo
| Challenge: | Despite LLMs' impressive capabilities in musical knowledge, music reasoning remains an unsolved task. |
| Approach: | They propose an open-source large language model (LLM) that integrates intrinsic musical abilities into LLaMA2 and GPT-3.5. |
| Outcome: | The proposed model can understand and generate music with a pure text tokenizer without external multi-modal neural structures or tokenizers. |
DeepGen: Diverse Search Ad Generation and Real-Time Customization (2022.emnlp-demos)
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| Challenge: | Existing systems that generate ads manually are not effective in generating ad copy and generating millions of ads for large businesses. |
| Approach: | They propose a system that generates fluent ads from advertiser’s web pages in an abstractive fashion and solves practical issues such as factuality and inference speed. |
| Outcome: | The proposed system generates fluent ads from advertiser’s web pages in an abstractive fashion and solves practical issues such as factuality and inference speed. |
Synchronous Double-channel Recurrent Network for Aspect-Opinion Pair Extraction (2020.acl-main)
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| Challenge: | Existing studies focus on aspect-opinion relation detection, but neglect to recognize the relations between aspects and opinion expressions. |
| Approach: | They propose a Synchronous Double-channel Recurrent Network to deal with AOPE task . they propose an opinion entity extraction unit, a relation detection unit, and a synchronization unit . |
| Outcome: | The proposed system achieves state-of-the-art in opinion entity extraction . it is based on three datasets based upon SemEval 2014 and 2015 benchmarks . |
Hiring Now: A Skill-Aware Multi-Attention Model for Job Posting Generation (2020.acl-main)
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| Challenge: | Creating job requirements is a crucial step in the recruiting process, but it is difficult to specify the level of education, experience, relevant skills per the job description. |
| Approach: | They propose a conditional text generation task to generate job requirements based on job descriptions . they use a hierarchical decoder to label the job description with multiple skills . a skill knowledge graph is constructed to capture the global prior knowledge about skills based upon the model . |
| Outcome: | The proposed method is evaluated on real-world job posting data. |