1,037 research outputs found
Quick and (not so) Dirty: Unsupervised Selection of Justification Sentences for Multi-hop Question Answering
We propose an unsupervised strategy for the selection of justification
sentences for multi-hop question answering (QA) that (a) maximizes the
relevance of the selected sentences, (b) minimizes the overlap between the
selected facts, and (c) maximizes the coverage of both question and answer.
This unsupervised sentence selection method can be coupled with any supervised
QA approach. We show that the sentences selected by our method improve the
performance of a state-of-the-art supervised QA model on two multi-hop QA
datasets: AI2's Reasoning Challenge (ARC) and Multi-Sentence Reading
Comprehension (MultiRC). We obtain new state-of-the-art performance on both
datasets among approaches that do not use external resources for training the
QA system: 56.82% F1 on ARC (41.24% on Challenge and 64.49% on Easy) and 26.1%
EM0 on MultiRC. Our justification sentences have higher quality than the
justifications selected by a strong information retrieval baseline, e.g., by
5.4% F1 in MultiRC. We also show that our unsupervised selection of
justification sentences is more stable across domains than a state-of-the-art
supervised sentence selection method.Comment: Published at EMNLP-IJCNLP 2019 as long conference paper. Corrected
the name reference for Speer et.al, 201
Unsupervised Alignment-based Iterative Evidence Retrieval for Multi-hop Question Answering
Evidence retrieval is a critical stage of question answering (QA), necessary
not only to improve performance, but also to explain the decisions of the
corresponding QA method. We introduce a simple, fast, and unsupervised
iterative evidence retrieval method, which relies on three ideas: (a) an
unsupervised alignment approach to soft-align questions and answers with
justification sentences using only GloVe embeddings, (b) an iterative process
that reformulates queries focusing on terms that are not covered by existing
justifications, which (c) a stopping criterion that terminates retrieval when
the terms in the given question and candidate answers are covered by the
retrieved justifications. Despite its simplicity, our approach outperforms all
the previous methods (including supervised methods) on the evidence selection
task on two datasets: MultiRC and QASC. When these evidence sentences are fed
into a RoBERTa answer classification component, we achieve state-of-the-art QA
performance on these two datasets.Comment: Accepted at ACL 2020 as a long conference pape
Exploiting Sentence Embedding for Medical Question Answering
Despite the great success of word embedding, sentence embedding remains a
not-well-solved problem. In this paper, we present a supervised learning
framework to exploit sentence embedding for the medical question answering
task. The learning framework consists of two main parts: 1) a sentence
embedding producing module, and 2) a scoring module. The former is developed
with contextual self-attention and multi-scale techniques to encode a sentence
into an embedding tensor. This module is shortly called Contextual
self-Attention Multi-scale Sentence Embedding (CAMSE). The latter employs two
scoring strategies: Semantic Matching Scoring (SMS) and Semantic Association
Scoring (SAS). SMS measures similarity while SAS captures association between
sentence pairs: a medical question concatenated with a candidate choice, and a
piece of corresponding supportive evidence. The proposed framework is examined
by two Medical Question Answering(MedicalQA) datasets which are collected from
real-world applications: medical exam and clinical diagnosis based on
electronic medical records (EMR). The comparison results show that our proposed
framework achieved significant improvements compared to competitive baseline
approaches. Additionally, a series of controlled experiments are also conducted
to illustrate that the multi-scale strategy and the contextual self-attention
layer play important roles for producing effective sentence embedding, and the
two kinds of scoring strategies are highly complementary to each other for
question answering problems.Comment: 8 page
Automatic Question Generation to Support Reading Comprehension of Learners - Content Selection, Neural Question Generation, and Educational Evaluation
Simply reading texts passively without actively engaging with their content is suboptimal for text comprehension since learners may miss crucial concepts or misunderstand essential ideas.
In contrast, engaging learners actively by asking questions fosters text comprehension.
However, educational resources frequently lack questions.
Textbooks often contain only a few at the end of a chapter, and informal learning resources such as Wikipedia lack them entirely.
Thus, in this thesis, we study to what extent questions about educational science texts can be automatically generated, tackling two research questions.
The first question concerns selecting learning-relevant passages to guide the generation process.
The second question investigates the generated questions' potential effects and applicability in reading comprehension scenarios.
Our first contribution improves the understanding of neural question generation's quality in education.
We find that the generators' high linguistic quality transfers to educational texts but that they require guidance by educational content selection.
In consequence, we study multiple educational context and answer selection mechanisms.
In our second contribution, we propose novel context selection approaches which target question-worthy sentences in texts.
In contrast to previous works, our context selectors are guided by educational theory.
The proposed methods perform competitive to related work while operating with educationally motivated decision criteria that are easier to understand for educational experts.
The third contribution addresses answer selection methods to guide neural question generation with expected answers.
Our experiments highlight the need for educational corpora for the task. Models trained on noneducational corpora do not transfer well to the educational domain.
Given this discrepancy, we propose a novel corpus construction approach.
It automatically derives educational answer selection corpora from textbooks.
We verify the approach's usefulness by showing that neural models trained on the constructed corpora learn to detect learning-relevant concepts.
In our last contribution, we use the insights from the previous experiments to design, implement, and evaluate an automatic question generator for educational use.
We evaluate the proposed generator intrinsically with an expert annotation study and extrinsically with an empirical reading comprehension study.
The two evaluation scenarios provide a nuanced view of the generated questions' strengths and weaknesses.
Expert annotations attribute an educational value to roughly 60 % of the questions but also reveal various ways in which the questions still fall short of the quality experts desire.
Furthermore, the reader-based evaluation indicates that the proposed educational question generator increases learning outcomes compared to a no-question control group.
In summary, the results of the thesis improve the understanding of the content selection tasks in educational question generation and provide evidence that it can improve reading comprehension.
As such, the proposed approaches are promising tools for authors and learners to promote active reading and thus foster text comprehension
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
Neural Natural Language Generation: A Survey on Multilinguality, Multimodality, Controllability and Learning
Developing artificial learning systems that can understand and generate natural language has been one of the long-standing goals of artificial intelligence. Recent decades have witnessed an impressive progress on both of these problems, giving rise to a new family of approaches. Especially, the advances in deep learning over the past couple of years have led to neural approaches to natural language generation (NLG). These methods combine generative language learning techniques with neural-networks based frameworks. With a wide range of applications in natural language processing, neural NLG (NNLG) is a new and fast growing field of research. In this state-of-the-art report, we investigate the recent developments and applications of NNLG in its full extent from a multidimensional view, covering critical perspectives such as multimodality, multilinguality, controllability and learning strategies. We summarize the fundamental building blocks of NNLG approaches from these aspects and provide detailed reviews of commonly used preprocessing steps and basic neural architectures. This report also focuses on the seminal applications of these NNLG models such as machine translation, description generation, automatic speech recognition, abstractive summarization, text simplification, question answering and generation, and dialogue generation. Finally, we conclude with a thorough discussion of the described frameworks by pointing out some open research directions.This work has been partially supported by the European Commission ICT COST Action âMulti-task, Multilingual, Multi-modal Language Generationâ (CA18231). AE was supported by BAGEP 2021 Award of the Science Academy. EE was supported in part by TUBA GEBIP 2018 Award. BP is in in part funded by Independent Research Fund Denmark (DFF) grant 9063-00077B. IC has received funding from the European Unionâs Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 838188. EL is partly funded by Generalitat Valenciana and the Spanish Government throught projects PROMETEU/2018/089 and RTI2018-094649-B-I00, respectively. SMI is partly funded by UNIRI project uniri-drustv-18-20. GB is partly supported by the Ministry of Innovation and the National Research, Development and Innovation Office within the framework of the Hungarian Artificial Intelligence National Laboratory Programme. COT is partially funded by the Romanian Ministry of European Investments and Projects through the Competitiveness Operational Program (POC) project âHOLOTRAINâ (grant no. 29/221 ap2/07.04.2020, SMIS code: 129077) and by the German Academic Exchange Service (DAAD) through the project âAWAKEN: content-Aware and netWork-Aware faKE News mitigationâ (grant no. 91809005). ESA is partially funded by the German Academic Exchange Service (DAAD) through the project âDeep-Learning Anomaly Detection for Human and Automated Users Behaviorâ (grant no. 91809358)
Recommending Personalized Summaries of Teaching Materials
Teaching activities have nowadays been supported by a variety of electronic devices. Formative assessment tools allow teachers to evaluate the level of understanding of learners during frontal lessons and to tailor the next teaching activities accordingly. Despite plenty of teaching materials are available in the textual form, manually exploring these very large collections of documents can be extremely time-consuming. The analysis of learner-produced data (e.g., test outcomes) can be exploited to recommend short extracts of teaching documents based on the actual learnerâs needs. This paper proposes a new methodology to recommend summaries of potentially large teaching documents. Summary recommendations are customized to studentâs needs according to the results of comprehension tests performed at the end of frontal lectures. Specifically, students undergo multiple-choice tests through a mobile application. In parallel, a set of topic-specific summaries of the teaching documents is generated. They consist of the most significant sentences related to a specific topic. According to the results of the tests, summaries are personally recommended to students. We assessed the applicability of the proposed approach in real context, i.e., a B.S. university-level course. The results achieved in the experimental evaluation confirmed its usability
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