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The Language Model Understood the Prompt was Ambiguous: Probing Syntactic Uncertainty Through Generation ...
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Does referent predictability affect the choice of referential form? A computational approach using masked coreference resolution ...
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Does referent predictability affect the choice of referential form? A computational approach using masked coreference resolution ...
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What do Entity-Centric Models Learn? Insights from Entity Linking in Multi-Party Dialogue ...
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Putting words in context: LSTM language models and lexical ambiguity ...
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Negated adjectives and antonyms in distributional semantics: not similar?
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Putting words in context: LSTM language models and lexical ambiguity
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Modeling word interpretation with deep language models: the interaction between expectations and lexical information
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AMORE-UPF at SemEval-2018 Task 4: BiLSTM with entity library
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Abstract:
Comunicació presentada al 12th International Workshop on Semantic Evaluation (SemEval-2018), celebrat els dies 5 i 6 de juny de 2018 a Nova Orleans, EUA. ; This paper describes our winning contribution to SemEval 2018 Task 4: Character Identification on Multiparty Dialogues. It is a simple, standard model with one key innovation, an entity library. Our results show that this innovation greatly facilitates the identification of infrequent characters. Because of the generic nature of our model, this finding is potentially relevant to any task that requires effective learning from sparse or unbalanced data. ; This project has received funding from the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme (grant agreement No 715154), and from the Spanish Ramón y Cajal programme (grant RYC-2015-18907).
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Keyword:
Computational linguistics; Computational semantics; Distributional semantics; Entities; Natural language processing; Reference; Semantic evaluation
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URL: http://hdl.handle.net/10230/35468
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How to represent a word and predict it, too: improving tied architectures for language modelling
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How to represent a word and predict it, too: improving tied architectures for language modelling
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What do entity-centric models learn? Insights from entity linking in multi-party dialogue
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