87 research outputs found
An Empirical Study of the Influence of User Tailoring on Evaluative Argument Effectiveness
The ability to generate effective evaluative arguments is critical for systems intended to advise and persuade their users. We have developed a system that generates evaluative arguments that are tailored to the user, properly arranged and concise. We have also devised an evaluation framework in which the effectiveness of evaluative arguments can be measured with real users. This paper presents the results of a formal experiment we performed in our framework to verify the influence of user tailoring on argument effectiveness.
Entity-based SpanCopy for Abstractive Summarization to Improve the Factual Consistency
Despite the success of recent abstractive summarizers on automatic evaluation
metrics, the generated summaries still present factual inconsistencies with the
source document. In this paper, we focus on entity-level factual inconsistency,
i.e. reducing the mismatched entities between the generated summaries and the
source documents. We therefore propose a novel entity-based SpanCopy mechanism,
and explore its extension with a Global Relevance component. Experiment results
on four summarization datasets show that SpanCopy can effectively improve the
entity-level factual consistency with essentially no change in the word-level
and entity-level saliency. The code is available at
https://github.com/Wendy-Xiao/Entity-based-SpanCop
Predicting Discourse Structure using Distant Supervision from Sentiment
Discourse parsing could not yet take full advantage of the neural NLP
revolution, mostly due to the lack of annotated datasets. We propose a novel
approach that uses distant supervision on an auxiliary task (sentiment
classification), to generate abundant data for RST-style discourse structure
prediction. Our approach combines a neural variant of multiple-instance
learning, using document-level supervision, with an optimal CKY-style tree
generation algorithm. In a series of experiments, we train a discourse parser
(for only structure prediction) on our automatically generated dataset and
compare it with parsers trained on human-annotated corpora (news domain RST-DT
and Instructional domain). Results indicate that while our parser does not yet
match the performance of a parser trained and tested on the same dataset
(intra-domain), it does perform remarkably well on the much more difficult and
arguably more useful task of inter-domain discourse structure prediction, where
the parser is trained on one domain and tested/applied on another one.Comment: Accepted to EMNLP 2019, 9 page
Unsupervised Learning of Discourse Structures using a Tree Autoencoder
Discourse information, as postulated by popular discourse theories, such as
RST and PDTB, has been shown to improve an increasing number of downstream NLP
tasks, showing positive effects and synergies of discourse with important
real-world applications. While methods for incorporating discourse become more
and more sophisticated, the growing need for robust and general discourse
structures has not been sufficiently met by current discourse parsers, usually
trained on small scale datasets in a strictly limited number of domains. This
makes the prediction for arbitrary tasks noisy and unreliable. The overall
resulting lack of high-quality, high-quantity discourse trees poses a severe
limitation to further progress. In order the alleviate this shortcoming, we
propose a new strategy to generate tree structures in a task-agnostic,
unsupervised fashion by extending a latent tree induction framework with an
auto-encoding objective. The proposed approach can be applied to any
tree-structured objective, such as syntactic parsing, discourse parsing and
others. However, due to the especially difficult annotation process to generate
discourse trees, we initially develop a method to generate larger and more
diverse discourse treebanks. In this paper we are inferring general tree
structures of natural text in multiple domains, showing promising results on a
diverse set of tasks.Comment: Accepted to AAAI 2021, 7 page
Large Discourse Treebanks from Scalable Distant Supervision
Discourse parsing is an essential upstream task in Natural Language
Processing with strong implications for many real-world applications. Despite
its widely recognized role, most recent discourse parsers (and consequently
downstream tasks) still rely on small-scale human-annotated discourse
treebanks, trying to infer general-purpose discourse structures from very
limited data in a few narrow domains. To overcome this dire situation and allow
discourse parsers to be trained on larger, more diverse and domain-independent
datasets, we propose a framework to generate "silver-standard" discourse trees
from distant supervision on the auxiliary task of sentiment analysis.Comment: Extended Abstract. Non Archival. 2 page
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