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Understanding the effects of negative (and positive) pointwise mutual information on word vectors
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Assessing idiomaticity representations in vector models with a noun compound dataset labeled at type and token levels
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AStitchInLanguageModels : dataset and methods for the exploration of idiomaticity in pre-trained language models
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CogNLP-Sheffield at CMCL 2021 Shared Task: Blending cognitively inspired features with transformer-based language models for predicting eye tracking patterns
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Investigating language impact in bilingual approaches for computational language documentation
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Unsupervised compositionality prediction of nominal compounds
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A dual-attention hierarchical recurrent neural network for dialogue act classification
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Abstract:
Recognising dialogue acts (DA) is important for many natural language processing tasks such as dialogue generation and intention recognition. In this paper, we propose a dual-attention hierarchical recurrent neural network for DA classification. Our model is partially inspired by the observation that conversational utterances are normally associated with both a DA and a topic, where the former captures the social act and the latter describes the subject matter. However, such a dependency between DAs and topics has not been utilised by most existing systems for DA classification. With a novel dual task-specific attention mechanism, our model is able, for utterances, to capture information about both DAs and topics, as well as information about the interactions between them. Experimental results show that by modelling topic as an auxiliary task, our model can significantly improve DA classification, yielding better or comparable performance to the state-of-the-art method on three public datasets.
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URL: https://eprints.whiterose.ac.uk/177032/ https://eprints.whiterose.ac.uk/177032/7/K19-1036.pdf https://aclanthology.org/K19-1036
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When the whole is greater than the sum of its parts : multiword expressions and idiomaticity
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Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
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Empirical evaluation of sequence-to-sequence models for word discovery in low-resource settings
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Similarity Measures for the Detection of Clinical Conditions with Verbal Fluency Tasks
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A corpus study of verbal multiword expressions in Brazilian Portuguese
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Unwritten languages demand attention too! Word discovery with encoder-decoder models
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Restricted recurrent neural tensor networks: Exploiting word frequency and compositionality
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UFRGS&LIF at SemEval-2016 task 10: Rule-based MWE identification and predominant-supersense tagging
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How naked is the naked truth? A multilingual lexicon of nominal compound compositionality
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