| 1. |
- Jorge, Emilio, 1992, et al.
(författare)
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Learning to Play Guess Who? and Inventing a Grounded Language as a Consequence.
- 2016
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Learning your first language is an incredible feat and not easily duplicated. Doing this using nothing but a few pictureless books, a corpus, would likely be impossible even for humans. As an alternative we propose to use situated interactions between agents as a driving force for communication, and the framework of Deep RecurrentQ-Networks (DRQN) for learning a common language grounded in the provided environment. We task the agents with interactive image search in the form of the game Guess Who?. The images from the game provide a non trivial environment for the agents to discuss and a natural grounding for the concepts they decide to encode in their communication. Our experiments show that it is possible to learn this task using DRQN and even more importantly that the words the agents use correspond to physical attributes present in the images that make up the agents environment.
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| 2. |
- Kågebäck, Mikael, 1981, et al.
(författare)
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Word Sense Disambiguation using a Bidirectional LSTM
- 2016
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Ingår i: 5th Workshop on Cognitive Aspects of the Lexicon (CogALex-V) at the 26th International Conference on Computational Linguistics (COLING 2016).
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Konferensbidrag (refereegranskat)abstract
- In this paper we present a clean, yet effective, model for word sense disambiguation. Our approach leverage a bidirectional long short-term memory network which is shared between all words. This enables the model to share statistical strength and to scale well with vocabularysize. The model is trained end-to-end, directly from the raw text to sense labels, and makes effective use of word order. We evaluate our approach on two standard datasets, using identical hyperparameter settings, which are in turn tuned on a third set of held out data. We employ no external resources (e.g. knowledge graphs, part-of-speech tagging, etc), language specific features, or hand crafted rules, but still achieve statistically equivalent results to the best state-of-the-art systems, that employ no such limitations.
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| 3. |
- Kågebäck, Mikael, 1981
(författare)
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Word Sense Embedded in Geometric Spaces - From Induction to Applications using Machine Learning
- 2016
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Words are not detached individuals but part of a beautiful interconnected web of related concepts, and to capture the full complexity of this web they need to be represented in a way that encapsulates all the semantic and syntactic facets of the language. Further, to enable computational processing they need to be expressed in a consistent manner so that similar properties are encoded in a similar way. In this thesis dense real valued vector representations, i.e. word embeddings, are extended and studied for their applicability to natural language processing (NLP). Word embeddings of two distinct flavors are presented as part of this thesis, sense aware word representations where different word senses are represented as distinct objects, and grounded word representations that are learned using multi-agent deep reinforcement learning to explicitly express properties of the physical world while the agents learn to play Guess Who?. The empirical usefulness of word embeddings are evaluated by employing them in a series of NLP related applications, i.e. word sense induction, word sense disambiguation, and automatic document summarisation. The results show great potential for word embeddings by outperforming previous state-of-the-art methods in two out of three applications, and achieving a statistically equivalent result in the third application but using a much simpler model than previous work.
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