Search Results for author:
Found 10 papers, 3 papers with code
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.
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.
Can Edge Probing Tasks Reveal Linguistic Knowledge in QA Models?
There have been many efforts to try to understand what gram-matical knowledge (e. g., ability to understand the part of speech of a token) is encoded in large pre-trained language models (LM).
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.
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.
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.
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.
Open Information Extraction from Question-Answer Pairs
We describe NeurON, a system for extracting tuples from question-answer pairs.
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.
