8 citations · 12 across the 6 of their papers we have counts for
7 papers
Machine Reading Comprehension using Case-based Reasoning
Dung Thai, Dhruv Agarwal, Mudit Chaudhary +6
We present an accurate and interpretable method for answer extraction in machine reading comprehension that is reminiscent of case-based reasoning (CBR) from classical AI. Our meth…
CBR-iKB: A Case-Based Reasoning Approach for Question Answering over Incomplete Knowledge Bases
Dung Thai, Srinivas Ravishankar, Ibrahim Abdelaziz +7
Knowledge bases (KBs) are often incomplete and constantly changing in practice. Yet, in many question answering applications coupled with knowledge bases, the sparse nature of KBs…
TABBIE: Pretrained Representations of Tabular Data
Hiroshi Iida, Dung Thai, Varun Manjunatha +1
Existing work on tabular representation learning jointly models tables and associated text using self-supervised objective functions derived from pretrained language models such as…
Case-based Reasoning for Natural Language Queries over Knowledge Bases
Rajarshi Das, Manzil Zaheer, Dung Thai +6
It is often challenging to solve a complex problem from scratch, but much easier if we can access other similar problems with their solutions -- a paradigm known as case-based reas…
Using BibTeX to Automatically Generate Labeled Data for Citation Field Extraction
Dung Thai, Zhiyang Xu, Nicholas Monath +2
Accurate parsing of citation reference strings is crucial to automatically construct scholarly databases such as Google Scholar or Semantic Scholar. Citation field extraction (CFE)…
Embedded-State Latent Conditional Random Fields for Sequence Labeling
Dung Thai, Sree Harsha Ramesh, Shikhar Murty +2
Complex textual information extraction tasks are often posed as sequence labeling or \emph{shallow parsing}, where fields are extracted using local labels made consistent through p…
Low-Rank Hidden State Embeddings for Viterbi Sequence Labeling
Dung Thai, Shikhar Murty, Trapit Bansal +3
In textual information extraction and other sequence labeling tasks it is now common to use recurrent neural networks (such as LSTM) to form rich embedded representations of long-t…