15,454 research outputs found
The First Shared Task on Discourse Representation Structure Parsing
The paper presents the IWCS 2019 shared task on semantic parsing where the
goal is to produce Discourse Representation Structures (DRSs) for English
sentences. DRSs originate from Discourse Representation Theory and represent
scoped meaning representations that capture the semantics of negation, modals,
quantification, and presupposition triggers. Additionally, concepts and
event-participants in DRSs are described with WordNet synsets and the thematic
roles from VerbNet. To measure similarity between two DRSs, they are
represented in a clausal form, i.e. as a set of tuples. Participant systems
were expected to produce DRSs in this clausal form. Taking into account the
rich lexical information, explicit scope marking, a high number of shared
variables among clauses, and highly-constrained format of valid DRSs, all these
makes the DRS parsing a challenging NLP task. The results of the shared task
displayed improvements over the existing state-of-the-art parser.Comment: International Conference on Computational Semantics (IWCS
Discourse Structure in Machine Translation Evaluation
In this article, we explore the potential of using sentence-level discourse
structure for machine translation evaluation. We first design discourse-aware
similarity measures, which use all-subtree kernels to compare discourse parse
trees in accordance with the Rhetorical Structure Theory (RST). Then, we show
that a simple linear combination with these measures can help improve various
existing machine translation evaluation metrics regarding correlation with
human judgments both at the segment- and at the system-level. This suggests
that discourse information is complementary to the information used by many of
the existing evaluation metrics, and thus it could be taken into account when
developing richer evaluation metrics, such as the WMT-14 winning combined
metric DiscoTKparty. We also provide a detailed analysis of the relevance of
various discourse elements and relations from the RST parse trees for machine
translation evaluation. In particular we show that: (i) all aspects of the RST
tree are relevant, (ii) nuclearity is more useful than relation type, and (iii)
the similarity of the translation RST tree to the reference tree is positively
correlated with translation quality.Comment: machine translation, machine translation evaluation, discourse
analysis. Computational Linguistics, 201
Implicit Discourse Relation Classification via Multi-Task Neural Networks
Without discourse connectives, classifying implicit discourse relations is a
challenging task and a bottleneck for building a practical discourse parser.
Previous research usually makes use of one kind of discourse framework such as
PDTB or RST to improve the classification performance on discourse relations.
Actually, under different discourse annotation frameworks, there exist multiple
corpora which have internal connections. To exploit the combination of
different discourse corpora, we design related discourse classification tasks
specific to a corpus, and propose a novel Convolutional Neural Network embedded
multi-task learning system to synthesize these tasks by learning both unique
and shared representations for each task. The experimental results on the PDTB
implicit discourse relation classification task demonstrate that our model
achieves significant gains over baseline systems.Comment: This is the pre-print version of a paper accepted by AAAI-1
Cross-lingual RST Discourse Parsing
Discourse parsing is an integral part of understanding information flow and
argumentative structure in documents. Most previous research has focused on
inducing and evaluating models from the English RST Discourse Treebank.
However, discourse treebanks for other languages exist, including Spanish,
German, Basque, Dutch and Brazilian Portuguese. The treebanks share the same
underlying linguistic theory, but differ slightly in the way documents are
annotated. In this paper, we present (a) a new discourse parser which is
simpler, yet competitive (significantly better on 2/3 metrics) to state of the
art for English, (b) a harmonization of discourse treebanks across languages,
enabling us to present (c) what to the best of our knowledge are the first
experiments on cross-lingual discourse parsing.Comment: To be published in EACL 2017, 13 page
Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification
Implicit discourse relation classification is of great challenge due to the
lack of connectives as strong linguistic cues, which motivates the use of
annotated implicit connectives to improve the recognition. We propose a feature
imitation framework in which an implicit relation network is driven to learn
from another neural network with access to connectives, and thus encouraged to
extract similarly salient features for accurate classification. We develop an
adversarial model to enable an adaptive imitation scheme through competition
between the implicit network and a rival feature discriminator. Our method
effectively transfers discriminability of connectives to the implicit features,
and achieves state-of-the-art performance on the PDTB benchmark.Comment: To appear in ACL201
GumDrop at the DISRPT2019 Shared Task: A Model Stacking Approach to Discourse Unit Segmentation and Connective Detection
In this paper we present GumDrop, Georgetown University's entry at the DISRPT
2019 Shared Task on automatic discourse unit segmentation and connective
detection. Our approach relies on model stacking, creating a heterogeneous
ensemble of classifiers, which feed into a metalearner for each final task. The
system encompasses three trainable component stacks: one for sentence
splitting, one for discourse unit segmentation and one for connective
detection. The flexibility of each ensemble allows the system to generalize
well to datasets of different sizes and with varying levels of homogeneity.Comment: Proceedings of Discourse Relation Parsing and Treebanking
(DISRPT2019
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Proceedings of QG2010: The Third Workshop on Question Generation
These are the peer-reviewed proceedings of "QG2010, The Third Workshop on Question Generation". The workshop included a special track for "QGSTEC2010: The First Question Generation Shared Task and Evaluation Challenge".
QG2010 was held as part of The Tenth International Conference on Intelligent Tutoring Systems (ITS2010)
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