Search Results for author:
Found 19 papers, 7 papers with code
Enhancing Multiple-choice Machine Reading Comprehension by Punishing Illogical Interpretations
Multiple-choice MRC is one of the most studied tasks in MRC due to the convenience of evaluation and the flexibility of answer format.
Biomedical Concept Normalization by Leveraging Hypernyms
Biomedical Concept Normalization (BCN) is widely used in biomedical text processing as a fundamental module.
CMQA: A Dataset of Conditional Question Answering with Multiple-Span Answers
To bridge the gap, we propose a new task: conditional question answering with hierarchical multi-span answers, where both the hierarchical relations and the conditions need to be extracted.
Scene Restoring for Narrative Machine Reading Comprehension
Inspired by this behavior of humans, we propose a method to let the machine imagine a scene during reading narrative for better comprehension.
Imagination Augmented Generation: Learning to Imagine Richer Context for Question Answering over Large Language Models
Retrieval-Augmented-Generation and Gener-ation-Augmented-Generation have been proposed to enhance the knowledge required for question answering over Large Language Models (LLMs).
Generative Calibration for In-context Learning
In this paper, we for the first time theoretically and empirically identify that such a paradox is mainly due to the label shift of the in-context model to the data distribution, in which LLMs shift the label marginal $p(y)$ while having a good label conditional $p(x|y)$.
MenatQA: A New Dataset for Testing the Temporal Comprehension and Reasoning Abilities of Large Language Models
As a result, it is natural for people to believe that LLMs have also mastered abilities such as time understanding and reasoning.
Interpreting Sentiment Composition with Latent Semantic Tree
As the key to sentiment analysis, sentiment composition considers the classification of a constituent via classifications of its contained sub-constituents and rules operated on them.
Unsupervised Text Style Transfer with Deep Generative Models
We present a general framework for unsupervised text style transfer with deep generative models.
Multi-View Graph Representation Learning for Answering Hybrid Numerical Reasoning Question
Hybrid question answering (HybridQA) over the financial report contains both textual and tabular data, and requires the model to select the appropriate evidence for the numerical reasoning task.
Graph Representation Learning
Machine Reading Comprehension
+1
TxAllo: Dynamic Transaction Allocation in Sharded Blockchain Systems
Blockchain sharding is a promising approach to this problem.
Generating Hierarchical Explanations on Text Classification Without Connecting Rules
The opaqueness of deep NLP models has motivated the development of methods for interpreting how deep models predict.
Logic Traps in Evaluating Attribution Scores
Meanwhile, since the reasoning process of deep models is inaccessible, researchers design various evaluation methods to demonstrate their arguments.
Alignment Rationale for Natural Language Inference
Deep learning models have achieved great success on the task of Natural Language Inference (NLI), though only a few attempts try to explain their behaviors.
MIE: A Medical Information Extractor towards Medical Dialogues
Electronic Medical Records (EMRs) have become key components of modern medical care systems.
Machine Reading Comprehension Using Structural Knowledge Graph-aware Network
Our method dynamically updates the representation of the knowledge according to the structural information of the constructed sub-graph.
An End-to-End Model for Question Answering over Knowledge Base with Cross-Attention Combining Global Knowledge
This simple representation strategy is not easy to express the proper information in the question.
Question Answering over Knowledge Base with Neural Attention Combining Global Knowledge Information
With the rapid growth of knowledge bases (KBs) on the web, how to take full advantage of them becomes increasingly important.
