Open-domain question answering is the task of question answering on open-domain datasets such as Wikipedia.
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In this work, we propose a simple and resource-efficient method to pretrain the paragraph encoder.
In this paper, we introduce AmbigQA, a new open-domain question answering task which involves predicting a set of question-answer pairs, where every plausible answer is paired with a disambiguated rewrite of the original question.
We present ktrain, a low-code Python library that makes machine learning more accessible and easier to apply.
Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de facto method.
Language model pre-training has been shown to capture a surprising amount of world knowledge, crucial for NLP tasks such as question answering.
Understanding natural language questions entails the ability to break down a question into the requisite steps for computing its answer.
We introduce an approach for open-domain question answering (QA) that retrieves and reads a passage graph, where vertices are passages of text and edges represent relationships that are derived from an external knowledge base or co-occurrence in the same article.
Open-domain question answering can be formulated as a phrase retrieval problem, in which we can expect huge scalability and speed benefit but often suffer from low accuracy due to the limitation of existing phrase representation models.
Recently, pre-trained models have achieved state-of-the-art results in various language understanding tasks, which indicates that pre-training on large-scale corpora may play a crucial role in natural language processing.
CHINESE NAMED ENTITY RECOGNITION CHINESE READING COMPREHENSION CHINESE SENTENCE PAIR CLASSIFICATION CHINESE SENTIMENT ANALYSIS LINGUISTIC ACCEPTABILITY MULTI-TASK LEARNING NATURAL LANGUAGE INFERENCE OPEN-DOMAIN QUESTION ANSWERING SEMANTIC TEXTUAL SIMILARITY SENTIMENT ANALYSIS