2 citations · 4 across the 10 of their papers we have counts for
10 papers
Learning to Summarize from LLM-generated Feedback
Hwanjun Song, Taewon Yun, Yuho Lee +4
Developing effective text summarizers remains a challenge due to issues like hallucinations, key information omissions, and verbosity in LLM-generated summaries. This work explores…
UniSumEval: Towards Unified, Fine-Grained, Multi-Dimensional Summarization Evaluation for LLMs
Yuho Lee, Taewon Yun, Jason Cai +2
Existing benchmarks for summarization quality evaluation often lack diverse input scenarios, focus on narrowly defined dimensions (e.g., faithfulness), and struggle with subjective…
FineSurE: Fine-grained Summarization Evaluation using LLMs
Hwanjun Song, Hang Su, Igor Shalyminov +2
Automated evaluation is crucial for streamlining text summarization benchmarking and model development, given the costly and time-consuming nature of human evaluation. Traditional…
CERET: Cost-Effective Extrinsic Refinement for Text Generation
Jason Cai, Hang Su, Monica Sunkara +2
Large Language Models (LLMs) are powerful models for generation tasks, but they may not generate good quality outputs in their first attempt. Apart from model fine-tuning, existing…
Semi-Supervised Dialogue Abstractive Summarization via High-Quality Pseudolabel Selection
Jianfeng He, Hang Su, Jason Cai +3
Semi-supervised dialogue summarization (SSDS) leverages model-generated summaries to reduce reliance on human-labeled data and improve the performance of summarization models. Whil…
Masked Audio Text Encoders are Effective Multi-Modal Rescorers
Jinglun Cai, Monica Sunkara, Xilai Li +3
Masked Language Models (MLMs) have proven to be effective for second-pass rescoring in Automatic Speech Recognition (ASR) systems. In this work, we propose Masked Audio Text Encode…
Mask The Bias: Improving Domain-Adaptive Generalization of CTC-based ASR with Internal Language Model Estimation
Nilaksh Das, Monica Sunkara, Sravan Bodapati +4
End-to-end ASR models trained on large amount of data tend to be implicitly biased towards language semantics of the training data. Internal language model estimation (ILME) has be…
Zero-Shot End-to-End Spoken Language Understanding via Cross-Modal Selective Self-Training
Jianfeng He, Julian Salazar, Kaisheng Yao +2
End-to-end (E2E) spoken language understanding (SLU) is constrained by the cost of collecting speech-semantics pairs, especially when label domains change. Hence, we explore \texti…
KG-ECO: Knowledge Graph Enhanced Entity Correction for Query Rewriting
Jinglun Cai, Mingda Li, Ziyan Jiang +5
Query Rewriting (QR) plays a critical role in large-scale dialogue systems for reducing frictions. When there is an entity error, it imposes extra challenges for a dialogue system…
Balancing Multi-Domain Corpora Learning for Open-Domain Response Generation
Yujie Xing, Jinglun Cai, Nils Barlaug +2
Open-domain conversational systems are assumed to generate equally good responses on multiple domains. Previous work achieved good performance on the single corpus, but training an…