This paper discusses models for dialogue state tracking using recurrent neural networks (RNN). We present experiments on the standard dialogue state tracking (DST) dataset, DSTC2. On the one hand, RNN models became the state of the art models in DST, on the other hand, most state-of-the-art models are only turn-based and require dataset-specific preprocessing (e.g. DSTC2-specific) in order to achieve such results. We implemented two architectures which can be used in incremental settings and require almost no preprocessing. We compare their performance to the benchmarks on DSTC2 and discuss their properties. With only trivial preprocessing, the performance of our models is close to the state-of- the-art results.
The system of achieves the state-of-the-art results and, similarly to our system, it predicts the dialogue state from words by employing a RNN. On the other hand, their system heavily relies on the user input abstraction. Another dialogue state tracker with LSTM was used in the reinforcement setting but the authors also used information from the SLU pipeline @cite_8 .
@cite_8
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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. We report evaluation results for real users of a learnt dialogue management policy versus a hand-coded policy in the TALK project's "Townlnfo" tourist information system. The learnt policy, for filling and confirming information slots, was derived from COMMUNICATOR (flight-booking) data using reinforcement learning (RL) as described in [2], ported to the tourist information domain (using a general method that we propose here), and tested using 18 human users in 180 dialogues, who also used a state-of-the-art hand- coded dialogue policy embedded in an otherwise identical system. We found that users of the (ported) learned policy had an average gain in perceived task completion of 14.2 (from 67.6 to 81.8 at p < .03), that the hand-coded policy dialogues had on average 3.3 more system turns (p < .01), and that the user satisfaction results were comparable, even though the policy was learned for a different domain. Combining these in a dialogue reward score, we found a 14.4 increase for the learnt policy (a 23.8 relative increase, p < .03). These results are important because they show a) that results for real users are consistent with results for automatic evaluation [2] of learned policies using simulated users [3, 4], b) that a policy learned using linear function approximation over a very large policy space [2] is effective for real users, and c) that policies learned using data for one domain can be used successfully in other domains. We also present a qualitative discussion of the learnt policy. We propose a method for learning dialogue management policies from a fixed data set. The method addresses the challenges posed by Information State Update (ISU)-based dialogue systems, which represent the state of a dialogue as a large set of features, resulting in a very large state space and a huge policy space. To address the problem that any fixed data set will only provide information about small portions of these state and policy spaces, we propose a hybrid model that combines reinforcement learning with supervised learning. The reinforcement learning is used to optimize a measure of dialogue reward, while the supervised learning is used to restrict the learned policy to the portions of these spaces for which we have data. We also use linear function approximation to address the need to generalize from a fixed amount of data to large state spaces. To demonstrate the effectiveness of this method on this challenging task, we trained this model on the COMMUNICATOR corpus, to which we have added annotations for user actions and Information States. When tested with a user simulation trained on a different part of the same data set, our hybrid model outperforms a pure supervised learning model and a pure reinforcement learning model. It also outperforms the hand-crafted systems on the COMMUNICATOR data, according to automatic evaluation measures, improving over the average COMMUNICATOR system policy by 10 . The proposed method will improve techniques for bootstrapping and automatic optimization of dialogue management policies from limited initial data sets. We model coherent conversation continuation via RNN-based dialogue models equipped with a dynamic attention mechanism. Our attention-RNN language model dynamically increases the scope of attention on the history as the conversation continues, as opposed to standard attention (or alignment) models with a fixed input scope in a sequence-to-sequence model. This allows each generated word to be associated with the most relevant words in its corresponding conversation history. We evaluate the model on two popular dialogue datasets, the open-domain MovieTriples dataset and the closed-domain Ubuntu Troubleshoot dataset, and achieve significant improvements over the state-of-the-art and baselines on several metrics, including complementary diversity-based metrics, human evaluation, and qualitative visualizations. We also show that a vanilla RNN with dynamic attention outperforms more complex memory models (e.g., LSTM and GRU) by allowing for flexible, long-distance memory. We promote further coherence via topic modeling-based reranking.