274 citations · 891 across the 147 of their papers we have counts for
21 papers · 2 filters
Continual Contrastive Finetuning Improves Low-Resource Relation Extraction
Wenxuan Zhou, Sheng Zhang, Tristan Naumann +2
Relation extraction (RE), which has relied on structurally annotated corpora for model training, has been particularly challenging in low-resource scenarios and domains. Recent lit…
Multi-hop Evidence Retrieval for Cross-document Relation Extraction
Keming Lu, I-Hung Hsu, Wenxuan Zhou +2
Relation Extraction (RE) has been extended to cross-document scenarios because many relations are not simply described in a single document. This inevitably brings the challenge of…
Can NLI Provide Proper Indirect Supervision for Low-resource Biomedical Relation Extraction?
Jiashu Xu, Mingyu Derek Ma, Muhao Chen
Two key obstacles in biomedical relation extraction (RE) are the scarcity of annotations and the prevalence of instances without explicitly pre-defined labels due to low annotation…
On-the-fly Denoising for Data Augmentation in Natural Language Understanding
Tianqing Fang, Wenxuan Zhou, Fangyu Liu +3
Data Augmentation (DA) is frequently used to provide additional training data without extra human annotation automatically. However, data augmentation may introduce noisy data that…
PINTO: Faithful Language Reasoning Using Prompt-Generated Rationales
Peifeng Wang, Aaron Chan, Filip Ilievski +2
Neural language models (LMs) have achieved impressive results on various language-based reasoning tasks by utilizing latent knowledge encoded in their own pretrained parameters. To…
Salience Allocation as Guidance for Abstractive Summarization
Fei Wang, Kaiqiang Song, Hongming Zhang +6
Abstractive summarization models typically learn to capture the salient information from scratch implicitly. Recent literature adds extractive summaries as guidance for abstractive…
SpaBERT: A Pretrained Language Model from Geographic Data for Geo-Entity Representation
Zekun Li, Jina Kim, Yao-Yi Chiang +1
Named geographic entities (geo-entities for short) are the building blocks of many geographic datasets. Characterizing geo-entities is integral to various application domains, such…
Extracting or Guessing? Improving Faithfulness of Event Temporal Relation Extraction
Haoyu Wang, Hongming Zhang, Yuqian Deng +3
In this paper, we seek to improve the faithfulness of TempRel extraction models from two perspectives. The first perspective is to extract genuinely based on contextual description…
Parameter-Efficient Tuning with Special Token Adaptation
Xiaocong Yang, James Y. Huang, Wenxuan Zhou +1
Parameter-efficient tuning aims at updating only a small subset of parameters when adapting a pretrained model to downstream tasks. In this work, we introduce PASTA, in which we on…
Are All Steps Equally Important? Benchmarking Essentiality Detection of Events
Haoyu Wang, Hongming Zhang, Yueguan Wang +3
Natural language expresses events with varying granularities, where coarse-grained events (goals) can be broken down into finer-grained event sequences (steps). A critical yet over…
VIPHY: Probing "Visible" Physical Commonsense Knowledge
Shikhar Singh, Ehsan Qasemi, Muhao Chen
In recent years, vision-language models (VLMs) have shown remarkable performance on visual reasoning tasks (e.g. attributes, location). While such tasks measure the requisite knowl…
PInKS: Preconditioned Commonsense Inference with Minimal Supervision
Ehsan Qasemi, Piyush Khanna, Qiang Ning +1
Reasoning with preconditions such as "glass can be used for drinking water unless the glass is shattered" remains an open problem for language models. The main challenge lies in th…
Robust (Controlled) Table-to-Text Generation with Structure-Aware Equivariance Learning
Fei Wang, Zhewei Xu, Pedro Szekely +1
Controlled table-to-text generation seeks to generate natural language descriptions for highlighted subparts of a table. Previous SOTA systems still employ a sequence-to-sequence g…
GRAPHCACHE: Message Passing as Caching for Sentence-Level Relation Extraction
Yiwei Wang, Muhao Chen, Wenxuan Zhou +3
Entity types and textual context are essential properties for sentence-level relation extraction (RE). Existing work only encodes these properties within individual instances, whic…
Should We Rely on Entity Mentions for Relation Extraction? Debiasing Relation Extraction with Counterfactual Analysis
Yiwei Wang, Muhao Chen, Wenxuan Zhou +6
Recent literature focuses on utilizing the entity information in the sentence-level relation extraction (RE), but this risks leaking superficial and spurious clues of relations. As…
Dangling-Aware Entity Alignment with Mixed High-Order Proximities
Juncheng Liu, Zequn Sun, Bryan Hooi +5
We study dangling-aware entity alignment in knowledge graphs (KGs), which is an underexplored but important problem. As different KGs are naturally constructed by different sets of…
Unified Semantic Typing with Meaningful Label Inference
James Y. Huang, Bangzheng Li, Jiashu Xu +1
Semantic typing aims at classifying tokens or spans of interest in a textual context into semantic categories such as relations, entity types, and event types. The inferred labels…
Does Your Model Classify Entities Reasonably? Diagnosing and Mitigating Spurious Correlations in Entity Typing
Nan Xu, Fei Wang, Bangzheng Li +2
Entity typing aims at predicting one or more words that describe the type(s) of a specific mention in a sentence. Due to shortcuts from surface patterns to annotated entity labels…
Summarization as Indirect Supervision for Relation Extraction
Keming Lu, I-Hung Hsu, Wenxuan Zhou +2
Relation extraction (RE) models have been challenged by their reliance on training data with expensive annotations. Considering that summarization tasks aim at acquiring concise ex…
Answer Consolidation: Formulation and Benchmarking
Wenxuan Zhou, Qiang Ning, Heba Elfardy +2
Current question answering (QA) systems primarily consider the single-answer scenario, where each question is assumed to be paired with one correct answer. However, in many real-wo…
Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference
Bangzheng Li, Wenpeng Yin, Muhao Chen
The task of ultra-fine entity typing (UFET) seeks to predict diverse and free-form words or phrases that describe the appropriate types of entities mentioned in sentences. A key ch…