Mukul Bhutani
2022
An Empirical study to understand the Compositional Prowess of Neural Dialog Models
Vinayshekhar Kumar
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Vaibhav Kumar
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Mukul Bhutani
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Alexander Rudnicky
Proceedings of the Third Workshop on Insights from Negative Results in NLP
In this work, we examine the problems associated with neural dialog models under the common theme of compositionality. Specifically, we investigate three manifestations of compositionality: (1) Productivity, (2) Substitutivity, and (3) Systematicity. These manifestations shed light on the generalization, syntactic robustness, and semantic capabilities of neural dialog models. We design probing experiments by perturbing the training data to study the above phenomenon. We make informative observations based on automated metrics and hope that this work increases research interest in understanding the capacity of these models.
2019
WriterForcing: Generating more interesting story endings
Prakhar Gupta
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Vinayshekhar Bannihatti Kumar
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Mukul Bhutani
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Alan W Black
Proceedings of the Second Workshop on Storytelling
We study the problem of generating interesting endings for stories. Neural generative models have shown promising results for various text generation problems. Sequence to Sequence (Seq2Seq) models are typically trained to generate a single output sequence for a given input sequence. However, in the context of a story, multiple endings are possible. Seq2Seq models tend to ignore the context and generate generic and dull responses. Very few works have studied generating diverse and interesting story endings for the same story context. In this paper, we propose models which generate more diverse and interesting outputs by 1) training models to focus attention on important keyphrases of the story, and 2) promoting generating nongeneric words. We show that the combination of the two leads to more interesting endings.
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