activity
20192023
most citedA Survey on In-context Learning

257 citations · 379 across the 36 of their papers we have counts for

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Showing 2020 · cs.CLShow all

7 papers · 2 filters

cs.CL2020

Generating Math Word Problems from Equations with Topic Controlling and Commonsense Enforcement

Tianyang Cao, Shuang Zeng, Songge Zhao +2

Recent years have seen significant advancement in text generation tasks with the help of neural language models. However, there exists a challenging task: generating math problem t…

cs.CL2020

Coarse-to-Fine Entity Representations for Document-level Relation Extraction

Damai Dai, Jing Ren, Shuang Zeng +2

Document-level Relation Extraction (RE) requires extracting relations expressed within and across sentences. Recent works show that graph-based methods, usually constructing a docu…

cs.CL2020

An Empirical Study on Model-agnostic Debiasing Strategies for Robust Natural Language Inference

Tianyu Liu, Xin Zheng, Xiaoan Ding +2

The prior work on natural language inference (NLI) debiasing mainly targets at one or few known biases while not necessarily making the models more robust. In this paper, we focus…

cs.CL2020

Discriminatively-Tuned Generative Classifiers for Robust Natural Language Inference

Xiaoan Ding, Tianyu Liu, Baobao Chang +2

While discriminative neural network classifiers are generally preferred, recent work has shown advantages of generative classifiers in term of data efficiency and robustness. In th…

cs.CL2020★ 12 cited

Double Graph Based Reasoning for Document-level Relation Extraction

Shuang Zeng, Runxin Xu, Baobao Chang +1

Document-level relation extraction aims to extract relations among entities within a document. Different from sentence-level relation extraction, it requires reasoning over multipl…

cs.CL2020★ 2 cited

Inductively Representing Out-of-Knowledge-Graph Entities by Optimal Estimation Under Translational Assumptions

Damai Dai, Hua Zheng, Fuli Luo +3

Conventional Knowledge Graph Completion (KGC) assumes that all test entities appear during training. However, in real-world scenarios, Knowledge Graphs (KG) evolve fast with out-of…