activity
20222026
most citedThe Model Arena for Cross-lingual Sentiment Analysis: A Comparative Study in the Era of Large Language Models

2 citations · 3 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CL2026

Topic Matching in the Wild: Benchmark and Lessons from Real-World ASR Transcripts

Saman Rahbar, Xiliang Zhu, Irvin Cardoza +1

In contact centers, real-time agent-assist tools determine, for each of many predefined topics, whether a live customer utterance is relevant and display a coaching card to the age…

cs.CL2025

How Accurate Are LLMs at Multi-Question Answering on Conversational Transcripts?

Xiliang Zhu, Shi Zong, David Rossouw

Deploying Large Language Models (LLMs) for question answering (QA) over lengthy contexts is a significant challenge. In industrial settings, this process is often hindered by high…

cs.CL2025★ 1 cited

Can Post-Training Quantization Benefit from an Additional QLoRA Integration?

Xiliang Zhu, Elena Khasanova, Cheng Chen

Large language models (LLMs) have transformed natural language processing but pose significant challenges for real-world deployment. These models necessitate considerable computing…

cs.CL2024★ 2 cited

The Model Arena for Cross-lingual Sentiment Analysis: A Comparative Study in the Era of Large Language Models

Xiliang Zhu, Shayna Gardiner, Tere Roldán +1

Sentiment analysis serves as a pivotal component in Natural Language Processing (NLP). Advancements in multilingual pre-trained models such as XLM-R and mT5 have contributed to the…

cs.CL2024

Resolving Transcription Ambiguity in Spanish: A Hybrid Acoustic-Lexical System for Punctuation Restoration

Xiliang Zhu, Chia-Tien Chang, Shayna Gardiner +2

Punctuation restoration is a crucial step after Automatic Speech Recognition (ASR) systems to enhance transcript readability and facilitate subsequent NLP tasks. Nevertheless, conv…

cs.CL2022

Extracting Similar Questions From Naturally-occurring Business Conversations

Xiliang Zhu, David Rossouw, Shayna Gardiner +1

Pre-trained contextualized embedding models such as BERT are a standard building block in many natural language processing systems. We demonstrate that the sentence-level represent…