Multi-hop Reading Comprehension Research Articles

Topic-aware multi-hop machine reading comprehension using weighted graphs

Topic-aware multi-hop machine reading comprehension using weighted graphs

Keywords-aware dynamic graph neural network for multi-hop reading comprehension

The multi-hop reading comprehension (RC) is challenging for machine reading comprehension. It is crucial for multi-hop RC to comprehend complex questions and contents between multiple paragraphs. In this paper, we propose a strategy of keywords-aware dynamic graph neural network (KA-DGN) to improve the performance of multi-hop reading comprehension. First of all, KA-DGN focuses on the salient information in the text, extracts keywords from the question and context. A window is specifically designed to frame the interrogative pronoun/adverb and its nearby words in the question, which encourages the model to focus on the answer. Next, the token-level answer span is predicted under the guidance of the keywords. And the boundary loss function is also invented to enhance the boundary awareness of the model on extracting the answer, which maximizes the probability of answer span bound while minimizing that of the noise. Finally, the model builds a dynamic reasoning graph combining explicit keywords and implicit semantic information among sentences. Graph neural network is applied to predict the sentence-level supporting facts. While evaluating on HotpotQA, the proposed KA-DGN achieves competitive performance in distractor setting.

Evidence Integration for Multi-Hop Reading Comprehension With Graph Neural Networks

Multi-hop reading comprehension focuses on one type of factoid question, where a system needs to properly integrate multiple pieces of evidence to correctly answer a question. Previous work approximates global evidence with local coreference information, encoding coreference chains with DAG-styled GRU layers within a gated-attention reader. However, coreference is limited in providing information for rich inference. We introduce a new method for better connecting global evidence, which forms more complex graphs compared to DAGs. To perform evidence integration on our graphs, we investigate two recent graph neural networks, namely graph convolutional network (GCN) and graph recurrent network (GRN). Experiments on two standard datasets show that richer global information leads to better answers. Our approach shows highly competitive performances on these datasets without deep language models (such as ELMo).

基于门控思想的改进交互关系提取的多跳阅读理解研究

基于门控思想的改进交互关系提取的多跳阅读理解研究

Summarize-then-Answer: Generating Concise Explanations for Multi-hop Reading Comprehension

How can we generate concise explanations for multi-hop Reading Comprehension (RC)? The current strategies of identifying supporting sentences can be seen as an extractive question-focused summarization of the input text. However, these extractive explanations are not necessarily concise i.e. not minimally sufficient for answering a question. Instead, we advocate for an abstractive approach, where we propose to generate a question-focused, abstractive summary of input paragraphs and then feed it to an RC system. Given a limited amount of human-annotated abstractive explanations, we train the abstractive explainer in a semi-supervised manner, where we start from the supervised model and then train it further through trial and error maximizing a conciseness-promoted reward function. Our experiments demonstrate that the proposed abstractive explainer can generate more compact explanations than an extractive explainer with limited supervision (only 2k instances) while maintaining sufficiency.

Contrastive heterogeneous graphs learning for multi-hop machine reading comprehension

Contrastive heterogeneous graphs learning for multi-hop machine reading comprehension

Multi-level retrieval with semantic Axiomatic Fuzzy Set clustering for question answering

This paper considers open-domain and multi-hop reading comprehension tasks that require complex multi-step reasoning processes. The study is particularly challenging because it requires a model to learn to explore “bridge” information to connect text snippets relevant to the answer. Unlike the usual neural-network-based retrieval models, which are difficult to interpret, this paper proposes a coarse-to-fine unsupervised evidence sentences retrieval model based on the Axiomatic Fuzzy Sets clustering with both reasoning ability and interpretability. According to the entities that appeared in a question, a chained inference retrieval was carried out to get the coarser candidate documents from knowledge bases. Then, sentence-level multi-feature scoring rules based on the part of speech and grammar are proposed. The Axiomatic Fuzzy Sets clustering algorithm based on the feature scores selects finer and sentence-level evidence by semantic descriptions. The retrieval process of the candidate sentences is unsupervised and straightforward, which does not require word embedding. Our model achieves state-of-the-art results in three open-domain QA datasets: HotpotQA, SQuAD Open and Natural Questions Open.

Select, Answer and Explain: Interpretable Multi-Hop Reading Comprehension over Multiple Documents

Interpretable multi-hop reading comprehension (RC) over multiple documents is a challenging problem because it demands reasoning over multiple information sources and explaining the answer prediction by providing supporting evidences. In this paper, we propose an effective and interpretable Select, Answer and Explain (SAE) system to solve the multi-document RC problem. Our system first filters out answer-unrelated documents and thus reduce the amount of distraction information. This is achieved by a document classifier trained with a novel pairwise learning-to-rank loss. The selected answer-related documents are then input to a model to jointly predict the answer and supporting sentences. The model is optimized with a multi-task learning objective on both token level for answer prediction and sentence level for supporting sentences prediction, together with an attention-based interaction between these two tasks. Evaluated on HotpotQA, a challenging multi-hop RC data set, the proposed SAE system achieves top competitive performance in distractor setting compared to other existing systems on the leaderboard.

A Sentence-Based Circular Reasoning Model in Multi-Hop Reading Comprehension

Multi-hop Machine Reading Comprehension (MRC) requires models to mine and utilize relevant information from multiple documents to predict the answer to a semantics-related query. The existing researches resort to either document-level or entity-level inferences among relevant information, but such practices may be too coarse or too subtle to result less accurate understanding of the text. To address this issue, this research proposes a Sentence-based Circular Reasoning (SCR) approach, which starts with sentence representation and then unites the query to establish a reasoning path based on a loop inference unit. Further, the model synthesizes the information existing in the reasoning path and receives a probability distribution for selecting the correct answer. In addition, this study proposes a nested mechanism to extend the probability distribution for weighting. And it is proven that this mechanism can assist the model to perform better. Some experiments evaluate SCR on two popular multi-hop MRC benchmark datasets, WikiHop and MedHop, achieving 71.6 and 63.2 in terms of accuracy, respectively, and thus exhibiting competitive results compared with the state-of-the-art model. Additionally, qualitative analyses also demonstrate the validity and interpretability of SCR.

Constructing Datasets for Multi-hop Reading Comprehension Across Documents

Most Reading Comprehension methods limit themselves to queries which can be answered using a single sentence, paragraph, or document. Enabling models to combine disjoint pieces of textual evidence would extend the scope of machine comprehension methods, but currently no resources exist to train and test this capability. We propose a novel task to encourage the development of models for text understanding across multiple documents and to investigate the limits of existing methods. In our task, a model learns to seek and combine evidence — effectively performing multihop, alias multi-step, inference. We devise a methodology to produce datasets for this task, given a collection of query-answer pairs and thematically linked documents. Two datasets from different domains are induced, and we identify potential pitfalls and devise circumvention strategies. We evaluate two previously proposed competitive models and find that one can integrate information across documents. However, both models struggle to select relevant information; and providing documents guaranteed to be relevant greatly improves their performance. While the models outperform several strong baselines, their best accuracy reaches 54.5% on an annotated test set, compared to human performance at 85.0%, leaving ample room for improvement.