19 research outputs found
Tree-Structured Neural Machine for Linguistics-Aware Sentence Generation
Different from other sequential data, sentences in natural language are
structured by linguistic grammars. Previous generative conversational models
with chain-structured decoder ignore this structure in human language and might
generate plausible responses with less satisfactory relevance and fluency. In
this study, we aim to incorporate the results from linguistic analysis into the
process of sentence generation for high-quality conversation generation.
Specifically, we use a dependency parser to transform each response sentence
into a dependency tree and construct a training corpus of sentence-tree pairs.
A tree-structured decoder is developed to learn the mapping from a sentence to
its tree, where different types of hidden states are used to depict the local
dependencies from an internal tree node to its children. For training
acceleration, we propose a tree canonicalization method, which transforms trees
into equivalent ternary trees. Then, with a proposed tree-structured search
method, the model is able to generate the most probable responses in the form
of dependency trees, which are finally flattened into sequences as the system
output. Experimental results demonstrate that the proposed X2Tree framework
outperforms baseline methods over 11.15% increase of acceptance ratio
Aiming to Know You Better Perhaps Makes Me a More Engaging Dialogue Partner
There have been several attempts to define a plausible motivation for a
chit-chat dialogue agent that can lead to engaging conversations. In this work,
we explore a new direction where the agent specifically focuses on discovering
information about its interlocutor. We formalize this approach by defining a
quantitative metric. We propose an algorithm for the agent to maximize it. We
validate the idea with human evaluation where our system outperforms various
baselines. We demonstrate that the metric indeed correlates with the human
judgments of engagingness.Comment: To appear in the proceedings of Conference on Computational Natural
Language Learning, CoNLL 201