Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Meta-Context Transformers for Domain-Specific Response Generation   Despite the tremendous success of neural dialogue models in recent years, it
suffers a lack of relevance, diversity, and some times coherence in generated
responses. Lately, transformer-based models, such as GPT-2, have revolutionized
the landscape of dialogue generation by capturing the long-range structures
through language modeling. Though these models have exhibited excellent
language coherence, they often lack relevance and terms when used for
domain-specific response generation. In this paper, we present DSRNet (Domain
Specific Response Network), a transformer-based model for dialogue response
generation by reinforcing domain-specific attributes. In particular, we extract
meta attributes from context and infuse them with the context utterances for
better attention over domain-specific key terms and relevance. We study the use
of DSRNet in a multi-turn multi-interlocutor environment for domain-specific
response generation. In our experiments, we evaluate DSRNet on Ubuntu dialogue
datasets, which are mainly composed of various technical domain related
dialogues for IT domain issue resolutions and also on CamRest676 dataset, which
contains restaurant domain conversations. Trained with maximum likelihood
objective, our model shows significant improvement over the state-of-the-art
for multi-turn dialogue systems supported by better BLEU and semantic
similarity (BertScore) scores. Besides, we also observe that the responses
produced by our model carry higher relevance due to the presence of
domain-specific key attributes that exhibit better overlap with the attributes
of the context. Our analysis shows that the performance improvement is mostly
due to the infusion of key terms along with dialogues which result in better
attention over domain-relevant terms. Other contributing factors include joint
modeling of dialogue context with the domain-specific meta attributes and
topics.

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The categories of this paper are: hep-ex
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The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an
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The categories of this paper are: eess
31
The categories of this paper are: ao-sci
12
The categories of this paper are: nucl-th
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The categories of this paper are: econ
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The categories of this paper are: q-alg
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The categories of this paper are: plasm-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: nlin
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The categories of this paper are: supr-con
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The categories of this paper are: solv-int
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The categories of this paper are: cond-mat
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-th