27 citations · 78 across the 21 of their papers we have counts for
15 papers · 1 filter
Alleviating Over-smoothing for Unsupervised Sentence Representation
Nuo Chen, Linjun Shou, Ming Gong +5
Currently, learning better unsupervised sentence representations is the pursuit of many natural language processing communities. Lots of approaches based on pre-trained language mo…
FiTs: Fine-grained Two-stage Training for Knowledge-aware Question Answering
Qichen Ye, Bowen Cao, Nuo Chen +2
Knowledge-aware question answering (KAQA) requires the model to answer questions over a knowledge base, which is essential for both open-domain QA and domain-specific QA, especiall…
Bridge the Gap between Language models and Tabular Understanding
Nuo Chen, Linjun Shou, Ming Gong +5
Table pretrain-then-finetune paradigm has been proposed and employed at a rapid pace after the success of pre-training in the natural language domain. Despite the promising finding…
Orca: A Few-shot Benchmark for Chinese Conversational Machine Reading Comprehension
Nuo Chen, Hongguang Li, Junqing He +8
The conversational machine reading comprehension (CMRC) task aims to answer questions in conversations, which has been a hot research topic in recent years because of its wide appl…
Natural Response Generation for Chinese Reading Comprehension
Nuo Chen, Hongguang Li, Yinan Bao +2
Machine reading comprehension (MRC) is an important area of conversation agents and draws a lot of attention. However, there is a notable limitation to current MRC benchmarks: The…
Large Language Models Meet Harry Potter: A Bilingual Dataset for Aligning Dialogue Agents with Characters
Nuo Chen, Yan Wang, Haiyun Jiang +5
In recent years, Dialogue-style Large Language Models (LLMs) such as ChatGPT and GPT4 have demonstrated immense potential in constructing open-domain dialogue agents. However, alig…
Exploring and Exploiting Multi-Granularity Representations for Machine Reading Comprehension
Nuo Chen, Chenyu You
Recently, the attention-enhanced multi-layer encoder, such as Transformer, has been extensively studied in Machine Reading Comprehension (MRC). To predict the answer, it is common…
End-to-end Spoken Conversational Question Answering: Task, Dataset and Model
Chenyu You, Nuo Chen, Fenglin Liu +3
In spoken question answering, the systems are designed to answer questions from contiguous text spans within the related speech transcripts. However, the most natural way that huma…
Bridging the Gap between Language Models and Cross-Lingual Sequence Labeling
Nuo Chen, Linjun Shou, Ming Gong +2
Large-scale cross-lingual pre-trained language models (xPLMs) have shown effectiveness in cross-lingual sequence labeling tasks (xSL), such as cross-lingual machine reading compreh…
Self-supervised Contrastive Cross-Modality Representation Learning for Spoken Question Answering
Chenyu You, Nuo Chen, Yuexian Zou
Spoken question answering (SQA) requires fine-grained understanding of both spoken documents and questions for the optimal answer prediction. In this paper, we propose novel traini…
Self-supervised Dialogue Learning for Spoken Conversational Question Answering
Nuo Chen, Chenyu You, Yuexian Zou
In spoken conversational question answering (SCQA), the answer to the corresponding question is generated by retrieving and then analyzing a fixed spoken document, including multi-…
Adaptive Bi-directional Attention: Exploring Multi-Granularity Representations for Machine Reading Comprehension
Nuo Chen, Fenglin Liu, Chenyu You +2
Recently, the attention-enhanced multi-layer encoder, such as Transformer, has been extensively studied in Machine Reading Comprehension (MRC). To predict the answer, it is common…
Towards Data Distillation for End-to-end Spoken Conversational Question Answering
Chenyu You, Nuo Chen, Fenglin Liu +2
In spoken question answering, QA systems are designed to answer questions from contiguous text spans within the related speech transcripts. However, the most natural way that human…
Knowledge Distillation for Improved Accuracy in Spoken Question Answering
Chenyu You, Nuo Chen, Yuexian Zou
Spoken question answering (SQA) is a challenging task that requires the machine to fully understand the complex spoken documents. Automatic speech recognition (ASR) plays a signifi…
Contextualized Attention-based Knowledge Transfer for Spoken Conversational Question Answering
Chenyu You, Nuo Chen, Yuexian Zou
Spoken conversational question answering (SCQA) requires machines to model complex dialogue flow given the speech utterances and text corpora. Different from traditional text quest…