793 research outputs found
Towards Understanding Egyptian Arabic Dialogues
Labelling of user's utterances to understanding his attends which called
Dialogue Act (DA) classification, it is considered the key player for dialogue
language understanding layer in automatic dialogue systems. In this paper, we
proposed a novel approach to user's utterances labeling for Egyptian
spontaneous dialogues and Instant Messages using Machine Learning (ML) approach
without relying on any special lexicons, cues, or rules. Due to the lack of
Egyptian dialect dialogue corpus, the system evaluated by multi-genre corpus
includes 4725 utterances for three domains, which are collected and annotated
manually from Egyptian call-centers. The system achieves F1 scores of 70. 36%
overall domains.Comment: arXiv admin note: substantial text overlap with arXiv:1505.0308
Learning Recursive Segments for Discourse Parsing
Automatically detecting discourse segments is an important preliminary step
towards full discourse parsing. Previous research on discourse segmentation
have relied on the assumption that elementary discourse units (EDUs) in a
document always form a linear sequence (i.e., they can never be nested).
Unfortunately, this assumption turns out to be too strong, for some theories of
discourse like SDRT allows for nested discourse units. In this paper, we
present a simple approach to discourse segmentation that is able to produce
nested EDUs. Our approach builds on standard multi-class classification
techniques combined with a simple repairing heuristic that enforces global
coherence. Our system was developed and evaluated on the first round of
annotations provided by the French Annodis project (an ongoing effort to create
a discourse bank for French). Cross-validated on only 47 documents (1,445
EDUs), our system achieves encouraging performance results with an F-score of
73% for finding EDUs.Comment: published at LREC 201
Cross-lingual and cross-domain discourse segmentation of entire documents
Discourse segmentation is a crucial step in building end-to-end discourse
parsers. However, discourse segmenters only exist for a few languages and
domains. Typically they only detect intra-sentential segment boundaries,
assuming gold standard sentence and token segmentation, and relying on
high-quality syntactic parses and rich heuristics that are not generally
available across languages and domains. In this paper, we propose statistical
discourse segmenters for five languages and three domains that do not rely on
gold pre-annotations. We also consider the problem of learning discourse
segmenters when no labeled data is available for a language. Our fully
supervised system obtains 89.5% F1 for English newswire, with slight drops in
performance on other domains, and we report supervised and unsupervised
(cross-lingual) results for five languages in total.Comment: To appear in Proceedings of ACL 201
Discourse structure and language technology
This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.An increasing number of researchers and practitioners in Natural Language Engineering face the prospect of having to work with entire texts, rather than individual sentences. While it is clear that text must have useful structure, its nature may be less clear, making it more difficult to exploit in applications. This survey of work on discourse structure thus provides a primer on the bases of which discourse is structured along with some of their formal properties. It then lays out the current state-of-the-art with respect to algorithms for recognizing these different structures, and how these algorithms are currently being used in Language Technology applications. After identifying resources that should prove useful in improving algorithm performance across a range of languages, we conclude by speculating on future discourse structure-enabled technology.Peer Reviewe
Dialogue Act Recognition via CRF-Attentive Structured Network
Dialogue Act Recognition (DAR) is a challenging problem in dialogue
interpretation, which aims to attach semantic labels to utterances and
characterize the speaker's intention. Currently, many existing approaches
formulate the DAR problem ranging from multi-classification to structured
prediction, which suffer from handcrafted feature extensions and attentive
contextual structural dependencies. In this paper, we consider the problem of
DAR from the viewpoint of extending richer Conditional Random Field (CRF)
structural dependencies without abandoning end-to-end training. We incorporate
hierarchical semantic inference with memory mechanism on the utterance
modeling. We then extend structured attention network to the linear-chain
conditional random field layer which takes into account both contextual
utterances and corresponding dialogue acts. The extensive experiments on two
major benchmark datasets Switchboard Dialogue Act (SWDA) and Meeting Recorder
Dialogue Act (MRDA) datasets show that our method achieves better performance
than other state-of-the-art solutions to the problem. It is a remarkable fact
that our method is nearly close to the human annotator's performance on SWDA
within 2% gap.Comment: 10 pages, 4figure
Splitting Arabic Texts into Elementary Discourse Units
International audienceIn this article, we propose the first work that investigates the feasibility of Arabic discourse segmentation into elementary discourse units within the segmented discourse representation theory framework. We first describe our annotation scheme that defines a set of principles to guide the segmentation process. Two corpora have been annotated according to this scheme: elementary school textbooks and newspaper documents extracted from the syntactically annotated Arabic Treebank. Then, we propose a multiclass supervised learning approach that predicts nested units. Our approach uses a combination of punctuation, morphological, lexical, and shallow syntactic features. We investigate how each feature contributes to the learning process. We show that an extensive morphological analysis is crucial to achieve good results in both corpora. In addition, we show that adding chunks does not boost the performance of our system
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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