Filtering noisy training data is one of the key approaches to improving the quality of neural network-based language generation. The dialogue research community especially suffers from a lack of less-noisy and sufficiently large data . In this work , we propose a scoring function that is specifically designed to identify low-quality utterance--response pairs to filter noisy training data. Our scoring function models the naturalness of the interconnection within dialogue pairs and their content-relatedness, which is based on previous findings in dialogue response generation and linguistics . We then demonstrate the effectiveness of our scoring function by confirming (i) the correlation between automatic scoring by the proposed function and human evaluation, and (ii) the performance of a dialogue response generator trained with filtered data. Furthermore, we experimentally confirm that our scoring function potentially works as a language-independent method .
Large-scale dialogue datasets have recently become available for training neural dialogue agents. However, these datasets have been reported to contain a non-negligible number of unacceptable utterance pairs . In this paper , we propose a method for scoring the quality of utterance pairs in terms of their connectivity and relatedness. The proposed scoring method is designed based on findings widely shared in the dialogue and linguistics research communities. We demonstrate that it has a relatively good correlation with the human judgment of dialogue quality . Furthermore, the method is applied to filter out potentially unacceptable utterance pairs from a large-scale noisy dialogue corpus to ensure its quality. We experimentally confirm that training data filtered by the proposed method improves the quality of neural dialogue agents in response generation .
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Filtering noisy training data is one of the key approaches to improving the quality of neural network-based language generation. The dialogue research community especially suffers from a lack of less-noisy and sufficiently large data
Large-scale dialogue datasets have recently become available for training neural dialogue agents. However, these datasets have been reported to contain a non-negligible number of unacceptable utterance pairs
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work
paper
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scoring function that is specifically designed to identify low-quality utterance--response pairs to filter noisy training data. Our scoring function models the naturalness of the interconnection within dialogue pairs and their content-relatedness, which is based on previous findings in
method for scoring the quality of utterance pairs in terms of their connectivity and relatedness. The proposed scoring method is designed based on findings widely shared in the dialogue and linguistics research communities. We demonstrate that it has a relatively good correlation with the human judgment of
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response generation and linguistics
quality
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We then demonstrate the effectiveness of our scoring function by confirming (i) the correlation between automatic scoring by the proposed function and human evaluation, and (ii) the performance of a dialogue response generator trained with filtered data.
R
we
the method is applied to filter out potentially unacceptable utterance pairs from a large-scale noisy dialogue corpus to ensure its quality. We
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our scoring function potentially works as a language-independent method
training data filtered by the proposed method improves the quality of neural dialogue agents in response generation