Search Results for author:
Found 19 papers, 9 papers with code
SubjQA: A Dataset for Subjectivity and Review Comprehension
We find that subjectivity is also an important feature in the case of QA, albeit with more intricate interactions between subjectivity and QA performance.
CompactIE: Compact Facts in Open Information Extraction
Our experiments on CaRB and Wire57 datasets indicate that CompactIE finds 1. 5x-2x more compact extractions than previous systems, with high precision, establishing a new state-of-the-art performance in OpenIE.
Ranked #3 on
Open Information Extraction
on BenchIE
Zero-shot Triplet Extraction by Template Infilling
We propose a novel framework, ZETT (ZEro-shot Triplet extraction by Template infilling), that aligns the task objective to the pre-training objective of generative transformers to generalize to unseen relations.
Ranked #1 on
Zero-shot Relation Triplet Extraction
on FewRel
XATU: A Fine-grained Instruction-based Benchmark for Explainable Text Updates
Text editing is a crucial task of modifying text to better align with user intents.
Less is More for Long Document Summary Evaluation by LLMs
Large Language Models (LLMs) have shown promising performance in summary evaluation tasks, yet they face challenges such as high computational costs and the Lost-in-the-Middle problem where important information in the middle of long documents is often overlooked.
Retrieval Helps or Hurts? A Deeper Dive into the Efficacy of Retrieval Augmentation to Language Models
We demonstrate the efficacy of our finer-grained metric and insights through an adaptive retrieval system that selectively employs retrieval and recall based on the frequencies of entities and relations in the question.
Sampo: Unsupervised Knowledge Base Construction for Opinions and Implications
Since it can be expensive to obtain training data to learn to extract implications for each new domain of reviews, we propose an unsupervised KBC system, Sampo, Specifically, Sampo is tailored to build KBs for domains where many reviews on the same domain are available.
Low-resource Entity Set Expansion: A Comprehensive Study on User-generated Text
Entity set expansion (ESE) aims at obtaining a more complete set of entities given a textual corpus and a seed set of entities of a concept.
Exploiting Structure in Representation of Named Entities using Active Learning
We show that programs for mapping entity mentions to their structures can be automatically generated using human-comprehensible labels.
Open Information Extraction from Question-Answer Pairs
We describe NeurON, a system for extracting tuples from question-answer pairs.
Answering Complex Questions by Combining Information from Curated and Extracted Knowledge Bases
Knowledge-based question answering (KB{\_}QA) has long focused on simple questions that can be answered from a single knowledge source, a manually curated or an automatically extracted KB.
Powering Effective Climate Communication with a Climate Knowledge Base
While many accept climate change and its growing impacts, few converse about it well, limiting the adoption speed of societal changes necessary to address it.
Can Edge Probing Tasks Reveal Linguistic Knowledge in QA Models?
We find that EP test results do not change significantly when the fine-tuned model performs well or in adversarial situations where the model is forced to learn wrong correlations.
Distilling Salient Reviews with Zero Labels
To alleviate this scarcity problem, we develop an unsupervised method, ZL-Distiller, which leverages contextual language representations of the reviews and their distributional patterns to identify salient sentences about entities.
Can Edge Probing Tests Reveal Linguistic Knowledge in QA Models?
We find that EP test results do not change significantly when the fine-tuned model performs well or in adversarial situations where the model is forced to learn wrong correlations.
Rethinking Language Models as Symbolic Knowledge Graphs
Despite these advancements, there is a void in comprehensively evaluating whether LMs can encompass the intricate topological and semantic attributes of KGs, attributes crucial for reasoning processes.
Distilling Large Language Models using Skill-Occupation Graph Context for HR-Related Tasks
We meticulously craft this benchmark to cater to a wide array of HR tasks, including matching and explaining resumes to job descriptions, extracting skills and experiences from resumes, and editing resumes.
Reasoning Capacity in Multi-Agent Systems: Limitations, Challenges and Human-Centered Solutions
We present a formal definition of reasoning capacity and illustrate its utility in identifying limitations within each component of the system.
