Papers by Jie Chi

9 papers
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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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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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.

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