2,092 research outputs found
Rhetorical relations for information retrieval
Typically, every part in most coherent text has some plausible reason for its
presence, some function that it performs to the overall semantics of the text.
Rhetorical relations, e.g. contrast, cause, explanation, describe how the parts
of a text are linked to each other. Knowledge about this socalled discourse
structure has been applied successfully to several natural language processing
tasks. This work studies the use of rhetorical relations for Information
Retrieval (IR): Is there a correlation between certain rhetorical relations and
retrieval performance? Can knowledge about a document's rhetorical relations be
useful to IR? We present a language model modification that considers
rhetorical relations when estimating the relevance of a document to a query.
Empirical evaluation of different versions of our model on TREC settings shows
that certain rhetorical relations can benefit retrieval effectiveness notably
(> 10% in mean average precision over a state-of-the-art baseline)
Parsing Argumentation Structures in Persuasive Essays
In this article, we present a novel approach for parsing argumentation
structures. We identify argument components using sequence labeling at the
token level and apply a new joint model for detecting argumentation structures.
The proposed model globally optimizes argument component types and
argumentative relations using integer linear programming. We show that our
model considerably improves the performance of base classifiers and
significantly outperforms challenging heuristic baselines. Moreover, we
introduce a novel corpus of persuasive essays annotated with argumentation
structures. We show that our annotation scheme and annotation guidelines
successfully guide human annotators to substantial agreement. This corpus and
the annotation guidelines are freely available for ensuring reproducibility and
to encourage future research in computational argumentation.Comment: Under review in Computational Linguistics. First submission: 26
October 2015. Revised submission: 15 July 201
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
Domain transfer for deep natural language generation from abstract meaning representations
Stochastic natural language generation systems that are trained from labelled datasets are often domainspecific in their annotation and in their mapping from semantic input representations to lexical-syntactic outputs. As a result, learnt models fail to generalize across domains, heavily restricting their usability beyond single applications. In this article, we focus on the problem of domain adaptation for natural language generation. We show how linguistic knowledge from a source domain, for which labelled data is available, can be adapted to a target domain by reusing training data across domains. As a key to this, we propose to employ abstract meaning representations as a common semantic representation across domains. We model natural language generation as a long short-term memory recurrent neural network encoderdecoder, in which one recurrent neural network learns a latent representation of a semantic input, and a second recurrent neural network learns to decode it to a sequence of words. We show that the learnt representations can be transferred across domains and can be leveraged effectively to improve training on new unseen domains. Experiments in three different domains and with six datasets demonstrate that the lexical-syntactic constructions learnt in one domain can be transferred to new domains and achieve up to 75-100% of the performance of in-domain training. This is based on objective metrics such as BLEU and semantic error rate and a subjective human rating study. Training a policy from prior knowledge from a different domain is consistently better than pure in-domain training by up to 10%
Adapt a Text-Oriented Chunker for Oral Data: How Much Manual Effort is Necessary?
International audienceIn this paper, we try three distinct approaches to chunk transcribed oral data with labeling tools learnt from a corpus of written texts. The purposeis to reach the best possible results with the least possible manual correction or re-learning effort
- …