1,590 research outputs found
Let Me Know What to Ask: Interrogative-Word-Aware Question Generation
Question Generation (QG) is a Natural Language Processing (NLP) task that
aids advances in Question Answering (QA) and conversational assistants.
Existing models focus on generating a question based on a text and possibly the
answer to the generated question. They need to determine the type of
interrogative word to be generated while having to pay attention to the grammar
and vocabulary of the question. In this work, we propose
Interrogative-Word-Aware Question Generation (IWAQG), a pipelined system
composed of two modules: an interrogative word classifier and a QG model. The
first module predicts the interrogative word that is provided to the second
module to create the question. Owing to an increased recall of deciding the
interrogative words to be used for the generated questions, the proposed model
achieves new state-of-the-art results on the task of QG in SQuAD, improving
from 46.58 to 47.69 in BLEU-1, 17.55 to 18.53 in BLEU-4, 21.24 to 22.33 in
METEOR, and from 44.53 to 46.94 in ROUGE-L.Comment: Accepted at 2nd Workshop on Machine Reading for Question Answering
(MRQA), EMNLP 201
Simulating the Machine Translation of Low-Resource Languages by Designing a Translator Between English and an Artificially Constructed Language
Natural language processing (NLP), or the use of computers to analyze natural language, is a field that relies heavily on syntax. It would seem intuitive that computers would thrive in this area due to their strict syntax requirements, but the syntax of natural languages leaves them unable to properly parse and generate sentences that seem normal to the average speaker. A subfield of NLP, machine translation, works mainly to computerize translation between different languages. Unfortunately, such translation is not without its weaknesses; language documentation is not created equal, and many low-resource languages—languages with relatively few kinds of documentation, most often written—are left with no way to effectively benefit from machine translation. As a step toward better translation processors for low-resource languages, this thesis examined the possibility of machine translation between high resource languages and low resource languages through an analysis of different machine learning techniques, and ultimately constructing a simple translator between English and an artificially constructed language using a context-free grammar (CFG)
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