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20222026
most citedWider and Deeper LLM Networks are Fairer LLM Evaluators

17 citations · 103 across the 53 of their papers we have counts for

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Showing 2022Show all

6 papers · 1 filter

cs.CL2022

Towards Generalized Open Information Extraction

Bowen Yu, Zhenyu Zhang, Jingyang Li +5

Open Information Extraction (OpenIE) facilitates the open-domain discovery of textual facts. However, the prevailing solutions evaluate OpenIE models on in-domain test sets aside f…

cs.CL2022★ 1 cited

Prompt Conditioned VAE: Enhancing Generative Replay for Lifelong Learning in Task-Oriented Dialogue

Yingxiu Zhao, Yinhe Zheng, Zhiliang Tian +6

Lifelong learning (LL) is vital for advanced task-oriented dialogue (ToD) systems. To address the catastrophic forgetting issue of LL, generative replay methods are widely employed…

cs.CL2022★ 1 cited

Semi-Supervised Lifelong Language Learning

Yingxiu Zhao, Yinhe Zheng, Bowen Yu +6

Lifelong learning aims to accumulate knowledge and alleviate catastrophic forgetting when learning tasks sequentially. However, existing lifelong language learning methods only foc…

cs.CL2022★ 1 cited

Estimating Soft Labels for Out-of-Domain Intent Detection

Hao Lang, Yinhe Zheng, Jian Sun +3

Out-of-Domain (OOD) intent detection is important for practical dialog systems. To alleviate the issue of lacking OOD training samples, some works propose synthesizing pseudo OOD s…

cs.CL2022

Dial2vec: Self-Guided Contrastive Learning of Unsupervised Dialogue Embeddings

Che Liu, Rui Wang, Junfeng Jiang +2

In this paper, we introduce the task of learning unsupervised dialogue embeddings. Trivial approaches such as combining pre-trained word or sentence embeddings and encoding through…

cs.CL2022★ 3 cited

Layout-Aware Information Extraction for Document-Grounded Dialogue: Dataset, Method and Demonstration

Zhenyu Zhang, Bowen Yu, Haiyang Yu +6

Building document-grounded dialogue systems have received growing interest as documents convey a wealth of human knowledge and commonly exist in enterprises. Wherein, how to compre…