Search Results for author:
Found 20 papers, 5 papers with code
Data Augmentation for Rare Symptoms in Vaccine Side-Effect Detection
We study the problem of entity detection and normalization applied to patient self-reports of symptoms that arise as side-effects of vaccines.
Joint Summarization-Entailment Optimization for Consumer Health Question Understanding
Understanding the intent of medical questions asked by patients, or Consumer Health Questions, is an essential skill for medical Conversational AI systems.
UCSD-Adobe at MEDIQA 2021: Transfer Learning and Answer Sentence Selection for Medical Summarization
We show that both transfer learning methods combined achieve the highest ROUGE scores.
On Linearizing Structured Data in Encoder-Decoder Language Models: Insights from Text-to-SQL
Structured data, prevalent in tables, databases, and knowledge graphs, poses a significant challenge in its representation.
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
Medical Question Understanding and Answering with Knowledge Grounding and Semantic Self-Supervision
The system first matches the summarized user question with an FAQ from a trusted medical knowledge base, and then retrieves a fixed number of relevant sentences from the corresponding answer document.
A Grounded Well-being Conversational Agent with Multiple Interaction Modes: Preliminary Results
Additionally, our agent has multiple interaction modes, that may give more options for the patient to use the agent, not just for medical question answering, but also to engage in conversations about general topics and current events.
A Gradually Soft Multi-Task and Data-Augmented Approach to Medical Question Understanding
Users of medical question answering systems often submit long and detailed questions, making it hard to achieve high recall in answer retrieval.
Recursive Tree-Structured Self-Attention for Answer Sentence Selection
The recursive nature of our model is able to represent all levels of syntactic parse trees with only one additional self-attention layer.
Interactive Plot Manipulation using Natural Language
This plotting model while accurate in most cases, still makes errors, therefore, the system allows a feedback mode, wherein the user is presented with a top-k list of plots, among which the user can pick the desired one.
Extracting the Unknown from Long Math Problems
In problem solving, understanding the problem that one seeks to solve is an essential initial step.
ChartDialogs: Plotting from Natural Language Instructions
This paper presents the problem of conversational plotting agents that carry out plotting actions from natural language instructions.
Rethinking Self-Attention: Towards Interpretability in Neural Parsing
Finally, we find that the Label Attention heads learn relations between syntactic categories and show pathways to analyze errors.
Ranked #1 on
Dependency Parsing
on Penn Treebank
Commonsense about Human Senses: Labeled Data Collection Processes
We consider the problem of extracting from text commonsense knowledge pertaining to human senses such as sound and smell.
Fine-Grained Spoiler Detection from Large-Scale Review Corpora
This paper presents computational approaches for automatically detecting critical plot twists in reviews of media products.
Unnamed Entity Recognition of Sense Mentions
We consider the problem of recognizing mentions of human senses in text.
Bilingual Dictionary Induction for Bantu Languages
We present a method for learning bilingual translation dictionaries between English and Bantu languages.
Sense Perception Common Sense Relationships
Often missing in existing knowledge bases of facts, are relationships that encode common sense knowledge about unnamed entities.
NORMA: Neighborhood Sensitive Maps for Multilingual Word Embeddings
Inducing multilingual word embeddings by learning a linear map between embedding spaces of different languages achieves remarkable accuracy on related languages.
Characterizing Departures from Linearity in Word Translation
We investigate the behavior of maps learned by machine translation methods.
