17 research outputs found
Putting the Horse Before the Cart:A Generator-Evaluator Framework for Question Generation from Text
Automatic question generation (QG) is a useful yet challenging task in NLP.
Recent neural network-based approaches represent the state-of-the-art in this
task. In this work, we attempt to strengthen them significantly by adopting a
holistic and novel generator-evaluator framework that directly optimizes
objectives that reward semantics and structure. The {\it generator} is a
sequence-to-sequence model that incorporates the {\it structure} and {\it
semantics} of the question being generated. The generator predicts an answer in
the passage that the question can pivot on. Employing the copy and coverage
mechanisms, it also acknowledges other contextually important (and possibly
rare) keywords in the passage that the question needs to conform to, while not
redundantly repeating words. The {\it evaluator} model evaluates and assigns a
reward to each predicted question based on its conformity to the {\it
structure} of ground-truth questions. We propose two novel QG-specific reward
functions for text conformity and answer conformity of the generated question.
The evaluator also employs structure-sensitive rewards based on evaluation
measures such as BLEU, GLEU, and ROUGE-L, which are suitable for QG. In
contrast, most of the previous works only optimize the cross-entropy loss,
which can induce inconsistencies between training (objective) and testing
(evaluation) measures. Our evaluation shows that our approach significantly
outperforms state-of-the-art systems on the widely-used SQuAD benchmark as per
both automatic and human evaluation.Comment: 10 pages, The SIGNLL Conference on Computational Natural Language
Learning (CoNLL 2019
Improving Question Generation with Sentence-level Semantic Matching and Answer Position Inferring
Taking an answer and its context as input, sequence-to-sequence models have
made considerable progress on question generation. However, we observe that
these approaches often generate wrong question words or keywords and copy
answer-irrelevant words from the input. We believe that lacking global question
semantics and exploiting answer position-awareness not well are the key root
causes. In this paper, we propose a neural question generation model with two
concrete modules: sentence-level semantic matching and answer position
inferring. Further, we enhance the initial state of the decoder by leveraging
the answer-aware gated fusion mechanism. Experimental results demonstrate that
our model outperforms the state-of-the-art (SOTA) models on SQuAD and MARCO
datasets. Owing to its generality, our work also improves the existing models
significantly.Comment: Revised version of paper accepted to Thirty-fourth AAAI Conference on
Artificial Intelligenc
EQG-RACE: Examination-Type Question Generation
Question Generation (QG) is an essential component of the automatic
intelligent tutoring systems, which aims to generate high-quality questions for
facilitating the reading practice and assessments. However, existing QG
technologies encounter several key issues concerning the biased and unnatural
language sources of datasets which are mainly obtained from the Web (e.g.
SQuAD). In this paper, we propose an innovative Examination-type Question
Generation approach (EQG-RACE) to generate exam-like questions based on a
dataset extracted from RACE. Two main strategies are employed in EQG-RACE for
dealing with discrete answer information and reasoning among long contexts. A
Rough Answer and Key Sentence Tagging scheme is utilized to enhance the
representations of input. An Answer-guided Graph Convolutional Network (AG-GCN)
is designed to capture structure information in revealing the inter-sentences
and intra-sentence relations. Experimental results show a state-of-the-art
performance of EQG-RACE, which is apparently superior to the baselines. In
addition, our work has established a new QG prototype with a reshaped dataset
and QG method, which provides an important benchmark for related research in
future work. We will make our data and code publicly available for further
research.Comment: Accepted by AAAI-202
A Dataset and Baselines for Visual Question Answering on Art
Answering questions related to art pieces (paintings) is a difficult task, as
it implies the understanding of not only the visual information that is shown
in the picture, but also the contextual knowledge that is acquired through the
study of the history of art. In this work, we introduce our first attempt
towards building a new dataset, coined AQUA (Art QUestion Answering). The
question-answer (QA) pairs are automatically generated using state-of-the-art
question generation methods based on paintings and comments provided in an
existing art understanding dataset. The QA pairs are cleansed by crowdsourcing
workers with respect to their grammatical correctness, answerability, and
answers' correctness. Our dataset inherently consists of visual
(painting-based) and knowledge (comment-based) questions. We also present a
two-branch model as baseline, where the visual and knowledge questions are
handled independently. We extensively compare our baseline model against the
state-of-the-art models for question answering, and we provide a comprehensive
study about the challenges and potential future directions for visual question
answering on art
What Do You Mean `Why?': Resolving Sluices in Conversations
In conversation, we often ask one-word questions such as `Why?' or `Who?'.
Such questions are typically easy for humans to answer, but can be hard for
computers, because their resolution requires retrieving both the right semantic
frames and the right arguments from context. This paper introduces the novel
ellipsis resolution task of resolving such one-word questions, referred to as
sluices in linguistics. We present a crowd-sourced dataset containing
annotations of sluices from over 4,000 dialogues collected from conversational
QA datasets, as well as a series of strong baseline architectures.Comment: Accepted at the 34TH AAAI Conference on Artificial Intelligence
(AAAI-2020