Question answering is the task of answering a question.
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Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on taskspecific datasets. We demonstrate that language models begin to learn these tasks without any explicit supervision when trained on a new dataset of millions of webpages called WebText.
Multimodal representation learning is gaining more and more interest within the deep learning community. We demonstrate the practical interest of our fusion model by using BLOCK for two challenging tasks: Visual Question Answering (VQA) and Visual Relationship Detection (VRD), where we design end-to-end learnable architectures for representing relevant interactions between modalities.
Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows. However, as deep learning models require a large amount of training data, applying deep learning to biomedical text mining is often unsuccessful due to the lack of training data in biomedical fields.
One of the challenges in large-scale information retrieval (IR) is to develop fine-grained and domain-specific methods to answer natural language questions. In this paper, we propose a novel QA approach based on Recognizing Question Entailment (RQE) and we describe the QA system and resources that we built and evaluated on real medical questions.
Thousands of complex natural language questions are submitted to community question answering websites on a daily basis, rendering them as one of the most important information sources these days. However, oftentimes submitted questions are unclear and cannot be answered without further clarification questions by expert community members.
We characterise some of the quirks and shortcomings in the exploration of Visual Dialogue (VD) - a sequential question-answering task where the questions and corresponding answers are related through given visual stimuli. To do so, we develop an embarrassingly simple method based on Canonical Correlation Analysis (CCA) that, on the standard dataset, achieves near state-of-the-art performance for some standard metric.
Question answering is one of the most important and difficult applications at the border of information retrieval and natural language processing, especially when we talk about complex science questions which require some form of inference to determine the correct answer. In this paper, we present a two-step method that combines information retrieval techniques optimized for question answering with deep learning models for natural language inference in order to tackle the multi-choice question answering in the science domain.
Second, these two tasks can benefit each other: answer selection can incorporate the external knowledge from knowledge base (KB), while KBQA can be improved by learning contextual information from answer selection. To fulfill the goal of jointly learning these two tasks, we propose a novel multi-task learning scheme that utilizes multi-view attention learned from various perspectives to enable these tasks to interact with each other as well as learn more comprehensive sentence representations.
Deep Learning NLP domain lacks procedures for the analysis of model robustness. In addition, we have created and published a new dataset that may be used for validation of robustness of a Q&A model.
This paper presents a deep sequential model for parsing discourse dependency structures of multi-party dialogues. For each EDU, the model decides to which previous EDU the current one should link and what the corresponding relation type is.