13 citations · 23 across the 9 of their papers we have counts for
9 papers
Gradient-Based Word Substitution for Obstinate Adversarial Examples Generation in Language Models
Yimu Wang, Peng Shi, Hongyang Zhang
In this paper, we study the problem of generating obstinate (over-stability) adversarial examples by word substitution in NLP, where input text is meaningfully changed but the mode…
Video-Text Retrieval by Supervised Sparse Multi-Grained Learning
Yimu Wang, Peng Shi
While recent progress in video-text retrieval has been advanced by the exploration of better representation learning, in this paper, we present a novel multi-grained sparse learnin…
Better Language Model with Hypernym Class Prediction
He Bai, Tong Wang, Alessandro Sordoni +1
Class-based language models (LMs) have been long devised to address context sparsity in -gram LMs. In this study, we revisit this approach in the context of neural LMs. We hypot…
Hierarchical Character Tagger for Short Text Spelling Error Correction
Mengyi Gao, Canran Xu, Peng Shi
State-of-the-art approaches to spelling error correction problem include Transformer-based Seq2Seq models, which require large training sets and suffer from slow inference time; an…
Mr. TyDi: A Multi-lingual Benchmark for Dense Retrieval
Xinyu Zhang, Xueguang Ma, Peng Shi +1
We present Mr. TyDi, a multi-lingual benchmark dataset for mono-lingual retrieval in eleven typologically diverse languages, designed to evaluate ranking with learned dense represe…
End-to-End Cross-Domain Text-to-SQL Semantic Parsing with Auxiliary Task
Peng Shi, Tao Yu, Patrick Ng +1
In this work, we focus on two crucial components in the cross-domain text-to-SQL semantic parsing task: schema linking and value filling. To encourage the model to learn better enc…
Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training
Peng Shi, Patrick Ng, Zhiguo Wang +5
Most recently, there has been significant interest in learning contextual representations for various NLP tasks, by leveraging large scale text corpora to train large neural langua…
Segatron: Segment-Aware Transformer for Language Modeling and Understanding
He Bai, Peng Shi, Jimmy Lin +5
Transformers are powerful for sequence modeling. Nearly all state-of-the-art language models and pre-trained language models are based on the Transformer architecture. However, it…
Semantics of the Unwritten: The Effect of End of Paragraph and Sequence Tokens on Text Generation with GPT2
He Bai, Peng Shi, Jimmy Lin +5
The semantics of a text is manifested not only by what is read, but also by what is not read. In this article, we will study how the implicit "not read" information such as end-of-…