5 research outputs found
Multi-Perspective Fusion Network for Commonsense Reading Comprehension
Commonsense Reading Comprehension (CRC) is a significantly challenging task,
aiming at choosing the right answer for the question referring to a narrative
passage, which may require commonsense knowledge inference. Most of the
existing approaches only fuse the interaction information of choice, passage,
and question in a simple combination manner from a \emph{union} perspective,
which lacks the comparison information on a deeper level. Instead, we propose a
Multi-Perspective Fusion Network (MPFN), extending the single fusion method
with multiple perspectives by introducing the \emph{difference} and
\emph{similarity} fusion\deleted{along with the \emph{union}}. More
comprehensive and accurate information can be captured through the three types
of fusion. We design several groups of experiments on MCScript dataset
\cite{Ostermann:LREC18:MCScript} to evaluate the effectiveness of the three
types of fusion respectively. From the experimental results, we can conclude
that the difference fusion is comparable with union fusion, and the similarity
fusion needs to be activated by the union fusion. The experimental result also
shows that our MPFN model achieves the state-of-the-art with an accuracy of
83.52\% on the official test set
Reasoning-Driven Question-Answering For Natural Language Understanding
Natural language understanding (NLU) of text is a fundamental challenge in AI, and it has received significant attention throughout the history of NLP research. This primary goal has been studied under different tasks, such as Question Answering (QA) and Textual Entailment (TE). In this thesis, we investigate the NLU problem through the QA task and focus on the aspects that make it a challenge for the current state-of-the-art technology. This thesis is organized into three main parts:
In the first part, we explore multiple formalisms to improve existing machine comprehension systems. We propose a formulation for abductive reasoning in natural language and show its effectiveness, especially in domains with limited training data. Additionally, to help reasoning systems cope with irrelevant or redundant information, we create a supervised approach to learn and detect the essential terms in questions.
In the second part, we propose two new challenge datasets. In particular, we create two datasets of natural language questions where (i) the first one requires reasoning over multiple sentences; (ii) the second one requires temporal common sense reasoning. We hope that the two proposed datasets will motivate the field to address more complex problems.
In the final part, we present the first formal framework for multi-step reasoning algorithms,
in the presence of a few important properties of language use, such as incompleteness, ambiguity, etc. We apply this framework to prove fundamental limitations for reasoning algorithms. These theoretical results provide extra intuition into the existing empirical evidence in the field