Simple is Better! Lightweight Data Augmentation for Low Resource Slot Filling and Intent Classification

Neural-based models have achieved outstanding performance on slot filling and intent classification, when fairly large in-domain training data are available. However, as new domains are frequently added, creating sizeable data is expensive. We show that lightweight augmentation, a set of augmentation methods involving word span and sentence level operations, alleviates data scarcity problems. Our experiments on limited data settings show that lightweight augmentation yields significant performance improvement on slot filling on the ATIS and SNIPS datasets, and achieves competitive performance with respect to more complex, state-of-the-art, augmentation approaches. Furthermore, lightweight augmentation is also beneficial when combined with pre-trained LM-based models, as it improves BERT-based joint intent and slot filling models.Comment: Accepted at PACLIC 2020 - The 34th Pacific Asia Conference on Language, Information and Computatio

Simulating Domain Changes in Conversational Agents Through Dialogue Adaptation

A major bottleneck for the large diffusion of data-driven conversational agents is that conversational domains are subject to continuous changes, which soon make initial dialogue models inadequate to manage new situations. In the current context, updating training data is usually carried on manually, and, in addition, there are no tools for simulating the impact of a certain domain change on the performance of the dialogue system. This position paper advocates that substantial progress in the capacity to simulate domain changes is based on the ability to automatically adapt training and test dialogues to those changes. We discuss the potential of a simulation framework for task-oriented dialogues, as well as the research challenges that need to be addressed

Domain-Aware Dialogue State Tracker for Multi-Domain Dialogue Systems

In task-oriented dialogue systems the dialogue state tracker (DST) component is responsible for predicting the state of the dialogue based on the dialogue history. Current DST approaches rely on a predefined domain ontology, a fact that limits their effective usage for large scale conversational agents, where the DST constantly needs to be interfaced with ever-increasing services and APIs. Focused towards overcoming this drawback, we propose a domain-aware dialogue state tracker, that is completely data-driven and it is modeled to predict for dynamic service schemas. The proposed model utilizes domain and slot information to extract both domain and slot specific representations for a given dialogue, and then uses such representations to predict the values of the corresponding slot. Integrating this mechanism with a pretrained language model (i.e. BERT), our approach can effectively learn semantic relations

Scalable Neural Dialogue State Tracking

A Dialogue State Tracker (DST) is a key component in a dialogue system aiming at estimating the beliefs of possible user goals at each dialogue turn. Most of the current DST trackers make use of recurrent neural networks and are based on complex architectures that manage several aspects of a dialogue, including the user utterance, the system actions, and the slot-value pairs defined in a domain ontology. However, the complexity of such neural architectures incurs into a considerable latency in the dialogue state prediction, which limits the deployments of the models in real-world applications, particularly when task scalability (i.e. amount of slots) is a crucial factor. In this paper, we propose an innovative neural model for dialogue state tracking, named Global encoder and Slot-Attentive decoders (G-SAT), which can predict the dialogue state with a very low latency time, while maintaining high-level performance. We report experiments on three different languages (English, Italian, and German) of the WoZ2.0 dataset, and show that the proposed approach provides competitive advantages over state-of-art DST systems, both in terms of accuracy and in terms of time complexity for predictions, being over 15 times faster than the other systems.Comment: 8 pages, 3 figures, Accepted at ASRU 201

A Robust Data-Driven Approach for Dialogue State Tracking of Unseen Slot Values

A Dialogue State Tracker is a key component in dialogue systems which estimates the beliefs of possible user goals at each dialogue turn. Deep learning approaches using recurrent neural networks have shown state-of-the-art performance for the task of dialogue state tracking. Generally, these approaches assume a predefined candidate list and struggle to predict any new dialogue state values that are not seen during training. This makes extending the candidate list for a slot without model retaining infeasible and also has limitations in modelling for low resource domains where training data for slot values are expensive. In this paper, we propose a novel dialogue state tracker based on copying mechanism that can effectively track such unseen slot values without compromising performance on slot values seen during training. The proposed model is also flexible in extending the candidate list without requiring any retraining or change in the model. We evaluate the proposed model on various benchmark datasets (DSTC2, DSTC3 and WoZ2.0) and show that our approach, outperform other end-to-end data-driven approaches in tracking unseen slot values and also provides significant advantages in modelling for DST

Linguistic based matching of local ontologies

This paper describes an automatic algorithm of meaning negotiation that enables semantic interoperability between local overlapping and heterogeneous ontologies. Rather than reconciling differences between heterogeneous ontologies, this algorithm searches for mappings between concepts of different ontologies. The algorithm is composed of three main steps: (i) computing the linguistic meaning of the label occurring in the ontologies via natural language processing, (ii) contextualization of such a linguistic meaning by considering the context, i.e. the ontologies, where a label occurs; (iii) comparing contextualized linguistic meaning of two ontologies in in order to find a possible matching between them

exploring named entity recognition as an auxiliary task for slot filling in conversational language understanding

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Decomposing Semantic Inferences

International audienceBeside formal approaches to semantic inference that rely on logical representation of meaning, the notion of Textual Entailment (TE) has been proposed as an applied framework to capture major semantic inference needs across applications in Computational Linguistics. Although several approaches have been tried and evaluation campaigns have shown improvements in TE, a renewed interest is rising in the research community towards a deeper and better understanding of the core phenomena involved in textual inference. Pursuing this direction, we are convinced that crucial progress will derive from a focus on decomposing the complexity of the TE task into basic phenomena and on their combination. In this paper, we carry out a deep analysis on TE data sets, investigating the relations among two relevant aspects of semantic inferences: the logical dimension, i.e. the capacity of the inference to prove the conclusion from its premises, and the linguistic dimension, i.e. the linguistic devices used to accomplish the goal of the inference. We propose a decomposition approach over TE pairs, where single linguistic phenomena are isolated in what we have called atomic inference pairs, and we show that at this granularity level the actual correlation between the linguistic and the logical dimensions of semantic inferences emerges and can be empirically observed

leveraging non conversational tasks for low resource slot filling does it help

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