| 1. |
- de Lhoneux, Miryam, 1990-, et al.
(författare)
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Recursive Subtree Composition in LSTM-Based Dependency Parsing
- 2019
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Ingår i: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics. - Stroudsburg : Association for Computational Linguistics. - 9781950737130 ; , s. 1566-1576
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Konferensbidrag (refereegranskat)abstract
- The need for tree structure modelling on top of sequence modelling is an open issue in neural dependency parsing. We investigate the impact of adding a tree layer on top of a sequential model by recursively composing subtree representations (composition) in a transition-based parser that uses features extracted by a BiLSTM. Composition seems superfluous with such a model, suggesting that BiLSTMs capture information about subtrees. We perform model ablations to tease out the conditions under which composition helps. When ablating the backward LSTM, performance drops and composition does not recover much of the gap. When ablating the forward LSTM, performance drops less dramatically and composition recovers a substantial part of the gap, indicating that a forward LSTM and composition capture similar information. We take the backward LSTM to be related to lookahead features and the forward LSTM to the rich history-based features both crucial for transition-based parsers. To capture history-based information, composition is better than a forward LSTM on its own, but it is even better to have a forward LSTM as part of a BiLSTM. We correlate results with language properties, showing that the improved lookahead of a backward LSTM is especially important for head-final languages.
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| 2. |
- Fraser, Kathleen, 1984, et al.
(författare)
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Multilingual prediction of Alzheimer’s disease through domain adaptation and concept-based language modelling
- 2019
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Ingår i: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), June 2 - June 7, 2019, Minneapolis, Minnesota / Jill Burstein, Christy Doran, Thamar Solorio (Editors). - Stroudsburg, PA : Association for Computational Linguistics. - 9781950737130
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Konferensbidrag (refereegranskat)abstract
- There is growing evidence that changes in speech and language may be early markers of dementia, but much of the previous NLP work in this area has been limited by the size of the available datasets. Here, we compare several methods of domain adaptation to augment a small French dataset of picture descriptions (n = 57) with a much larger English dataset (n = 550), for the task of automatically distinguishing participants with dementia from controls. The first challenge is to identify a set of features that transfer across languages; in addition to previously used features based on information units, we introduce a new set of features to model the order in which information units are produced by dementia patients and controls. These concept-based language model features improve classification performance in both English and French separately, and the best result (AUC = 0.89) is achieved using the multilingual training set with a combination of information and language model features.
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