output
20052026
most citedFirst-principles calculations of charge carrier mobility and conductivity in bulk semiconductors and two-dimensional materials

351 citations

Showing 2020 · cs.CLShow all

11 papers · 2 filters

cs.CL2020★ 4 cited

Natural Language Inference in Context -- Investigating Contextual Reasoning over Long Texts

Hanmeng Liu, Leyang Cui, Jian Liu +1

Natural language inference (NLI) is a fundamental NLP task, investigating the entailment relationship between two texts. Popular NLI datasets present the task at sentence-level. Wh…

cs.CL2020★ 14 cited

Dialogue State Induction Using Neural Latent Variable Models

Qingkai Min, Libo Qin, Zhiyang Teng +2

Dialogue state modules are a useful component in a task-oriented dialogue system. Traditional methods find dialogue states by manually labeling training corpora, upon which neural…

cs.CL2020

On Commonsense Cues in BERT for Solving Commonsense Tasks

Leyang Cui, Sijie Cheng, Yu Wu +1

BERT has been used for solving commonsense tasks such as CommonsenseQA. While prior research has found that BERT does contain commonsense information to some extent, there has been…

cs.CL2020★ 5 cited

LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning

Jian Liu, Leyang Cui, Hanmeng Liu +3

Machine reading is a fundamental task for testing the capability of natural language understanding, which is closely related to human cognition in many aspects. With the rising of…

cs.CL2020★ 11 cited

CoSDA-ML: Multi-Lingual Code-Switching Data Augmentation for Zero-Shot Cross-Lingual NLP

Libo Qin, Minheng Ni, Yue Zhang +1

Multi-lingual contextualized embeddings, such as multilingual-BERT (mBERT), have shown success in a variety of zero-shot cross-lingual tasks. However, these models are limited by h…

cs.CL2020

DRTS Parsing with Structure-Aware Encoding and Decoding

Qiankun Fu, Yue Zhang, Jiangming Liu +1

Discourse representation tree structure (DRTS) parsing is a novel semantic parsing task which has been concerned most recently. State-of-the-art performance can be achieved by a ne…

cs.CL2020★ 1 cited

Exploiting Syntactic Structure for Better Language Modeling: A Syntactic Distance Approach

Wenyu Du, Zhouhan Lin, Yikang Shen +3

It is commonly believed that knowledge of syntactic structure should improve language modeling. However, effectively and computationally efficiently incorporating syntactic structu…

cs.CL2020★ 2 cited

MuTual: A Dataset for Multi-Turn Dialogue Reasoning

Leyang Cui, Yu Wu, Shujie Liu +2

Non-task oriented dialogue systems have achieved great success in recent years due to largely accessible conversation data and the development of deep learning techniques. Given a…

cs.CL2020★ 24 cited

News-Driven Stock Prediction With Attention-Based Noisy Recurrent State Transition

Xiao Liu, Heyan Huang, Yue Zhang +1

We consider direct modeling of underlying stock value movement sequences over time in the news-driven stock movement prediction. A recurrent state transition model is constructed,…

cs.CL2020★ 101 cited

Investigating Typed Syntactic Dependencies for Targeted Sentiment Classification Using Graph Attention Neural Network

Xuefeng Bai, Pengbo Liu, Yue Zhang

Targeted sentiment classification predicts the sentiment polarity on given target mentions in input texts. Dominant methods employ neural networks for encoding the input sentence a…

cs.CL2020★ 67 cited

Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs

Leonardo F. R. Ribeiro, Yue Zhang, Claire Gardent +1

Recent graph-to-text models generate text from graph-based data using either global or local aggregation to learn node representations. Global node encoding allows explicit communi…