66,791 research outputs found
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%
Challenging Neural Dialogue Models with Natural Data: Memory Networks Fail on Incremental Phenomena
Natural, spontaneous dialogue proceeds incrementally on a word-by-word basis;
and it contains many sorts of disfluency such as mid-utterance/sentence
hesitations, interruptions, and self-corrections. But training data for machine
learning approaches to dialogue processing is often either cleaned-up or wholly
synthetic in order to avoid such phenomena. The question then arises of how
well systems trained on such clean data generalise to real spontaneous
dialogue, or indeed whether they are trainable at all on naturally occurring
dialogue data. To answer this question, we created a new corpus called bAbI+ by
systematically adding natural spontaneous incremental dialogue phenomena such
as restarts and self-corrections to the Facebook AI Research's bAbI dialogues
dataset. We then explore the performance of a state-of-the-art retrieval model,
MemN2N, on this more natural dataset. Results show that the semantic accuracy
of the MemN2N model drops drastically; and that although it is in principle
able to learn to process the constructions in bAbI+, it needs an impractical
amount of training data to do so. Finally, we go on to show that an
incremental, semantic parser -- DyLan -- shows 100% semantic accuracy on both
bAbI and bAbI+, highlighting the generalisation properties of linguistically
informed dialogue models.Comment: 9 pages, 3 figures, 2 tables. Accepted as a full paper for SemDial
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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)
Joint Modeling of Content and Discourse Relations in Dialogues
We present a joint modeling approach to identify salient discussion points in
spoken meetings as well as to label the discourse relations between speaker
turns. A variation of our model is also discussed when discourse relations are
treated as latent variables. Experimental results on two popular meeting
corpora show that our joint model can outperform state-of-the-art approaches
for both phrase-based content selection and discourse relation prediction
tasks. We also evaluate our model on predicting the consistency among team
members' understanding of their group decisions. Classifiers trained with
features constructed from our model achieve significant better predictive
performance than the state-of-the-art.Comment: Accepted by ACL 2017. 11 page
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