1 |
Multilingual and Cross-Lingual Intent Detection from Spoken Data ...
|
|
|
|
BASE
|
|
Show details
|
|
2 |
Multilingual and Cross-Lingual Intent Detection from Spoken Data ...
|
|
|
|
BASE
|
|
Show details
|
|
4 |
Data-Driven Language Understanding for Spoken Dialogue Systems ...
|
|
|
|
BASE
|
|
Show details
|
|
5 |
Adversarial Propagation and Zero-Shot Cross-Lingual Transfer of Word Vector Specialization ...
|
|
|
|
BASE
|
|
Show details
|
|
6 |
Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources ...
|
|
|
|
BASE
|
|
Show details
|
|
7 |
Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources ...
|
|
|
|
BASE
|
|
Show details
|
|
8 |
Post-specialisation: Retrofitting vectors of words unseen in lexical resources
|
|
|
|
BASE
|
|
Show details
|
|
9 |
Adversarial propagation and zero-shot cross-lingual transfer of word vector specialization
|
|
|
|
BASE
|
|
Show details
|
|
11 |
Semantic Specialisation of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints ...
|
|
|
|
BASE
|
|
Show details
|
|
12 |
Morph-fitting: Fine-Tuning Word Vector Spaces with Simple Language-Specific Rules ...
|
|
|
|
BASE
|
|
Show details
|
|
13 |
Cross-Lingual Induction and Transfer of Verb Classes Based on Word Vector Space Specialisation ...
|
|
|
|
BASE
|
|
Show details
|
|
14 |
Semantic Specialisation of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints ...
|
|
|
|
BASE
|
|
Show details
|
|
15 |
Semantic Specialisation of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints
|
|
Mrkšić, Nikola; Vulić, Ivan; Ó Séaghdha, Diarmuid. - : Association for Computational Linguistics, 2017. : https://www.transacl.org/ojs/index.php/tacl/article/view/1171, 2017. : Transactions of the Association for Computational Linguistics (TACL), 2017
|
|
BASE
|
|
Show details
|
|
17 |
Neural Belief Tracker: Data-Driven Dialogue State Tracking ...
|
|
|
|
Abstract:
One of the core components of modern spoken dialogue systems is the belief tracker, which estimates the user's goal at every step of the dialogue. However, most current approaches have difficulty scaling to larger, more complex dialogue domains. This is due to their dependency on either: a) Spoken Language Understanding models that require large amounts of annotated training data; or b) hand-crafted lexicons for capturing some of the linguistic variation in users' language. We propose a novel Neural Belief Tracking (NBT) framework which overcomes these problems by building on recent advances in representation learning. NBT models reason over pre-trained word vectors, learning to compose them into distributed representations of user utterances and dialogue context. Our evaluation on two datasets shows that this approach surpasses past limitations, matching the performance of state-of-the-art models which rely on hand-crafted semantic lexicons and outperforming them when such lexicons are not provided. ... : Accepted as a long paper for the 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017) ...
|
|
Keyword:
Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG
|
|
URL: https://arxiv.org/abs/1606.03777 https://dx.doi.org/10.48550/arxiv.1606.03777
|
|
BASE
|
|
Hide details
|
|
18 |
Research data supporting "On-line Active Reward Learning for Policy Optimisation in Spoken Dialogue Systems"
|
|
|
|
BASE
|
|
Show details
|
|
19 |
Multi-domain Dialog State Tracking using Recurrent Neural Networks ...
|
|
|
|
BASE
|
|
Show details
|
|
20 |
Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking ...
|
|
|
|
BASE
|
|
Show details
|
|
|
|