40 citations · 101 across the 19 of their papers we have counts for
12 papers · 1 filter
Code Representation Learning At Scale
Dejiao Zhang, Wasi Ahmad, Ming Tan +5
Recent studies have shown that code language models at scale demonstrate significant performance gains on downstream tasks, i.e., code generation. However, most of the existing wor…
Efficient Shapley Values Estimation by Amortization for Text Classification
Chenghao Yang, Fan Yin, He He +3
Despite the popularity of Shapley Values in explaining neural text classification models, computing them is prohibitive for large pretrained models due to a large number of model e…
STREET: A Multi-Task Structured Reasoning and Explanation Benchmark
Danilo Ribeiro, Shen Wang, Xiaofei Ma +10
We introduce STREET, a unified multi-task and multi-domain natural language reasoning and explanation benchmark. Unlike most existing question-answering (QA) datasets, we expect mo…
SWING: Balancing Coverage and Faithfulness for Dialogue Summarization
Kung-Hsiang Huang, Siffi Singh, Xiaofei Ma +5
Missing information is a common issue of dialogue summarization where some information in the reference summaries is not covered in the generated summaries. To address this issue,…
ContraCLM: Contrastive Learning For Causal Language Model
Nihal Jain, Dejiao Zhang, Wasi Uddin Ahmad +9
Despite exciting progress in causal language models, the expressiveness of the representations is largely limited due to poor discrimination ability. To remedy this issue, we prese…
Learning Dialogue Representations from Consecutive Utterances
Zhihan Zhou, Dejiao Zhang, Wei Xiao +4
Learning high-quality dialogue representations is essential for solving a variety of dialogue-oriented tasks, especially considering that dialogue systems often suffer from data sc…
Entailment Tree Explanations via Iterative Retrieval-Generation Reasoner
Danilo Ribeiro, Shen Wang, Xiaofei Ma +8
Large language models have achieved high performance on various question answering (QA) benchmarks, but the explainability of their output remains elusive. Structured explanations,…
Contrastive Document Representation Learning with Graph Attention Networks
Peng Xu, Xinchi Chen, Xiaofei Ma +2
Recent progress in pretrained Transformer-based language models has shown great success in learning contextual representation of text. However, due to the quadratic self-attention…
Virtual Augmentation Supported Contrastive Learning of Sentence Representations
Dejiao Zhang, Wei Xiao, Henghui Zhu +2
Despite profound successes, contrastive representation learning relies on carefully designed data augmentations using domain specific knowledge. This challenge is magnified in natu…
Beyond [CLS] through Ranking by Generation
Cicero Nogueira dos Santos, Xiaofei Ma, Ramesh Nallapati +2
Generative models for Information Retrieval, where ranking of documents is viewed as the task of generating a query from a document's language model, were very successful in variou…
Universal Text Representation from BERT: An Empirical Study
Xiaofei Ma, Zhiguo Wang, Patrick Ng +2
We present a systematic investigation of layer-wise BERT activations for general-purpose text representations to understand what linguistic information they capture and how transfe…
Multi-passage BERT: A Globally Normalized BERT Model for Open-domain Question Answering
Zhiguo Wang, Patrick Ng, Xiaofei Ma +2
BERT model has been successfully applied to open-domain QA tasks. However, previous work trains BERT by viewing passages corresponding to the same question as independent training…