Deploying task-oriented dialog (TOD) systems for new domains and tasks requires natural language understanding models that are 1) resource-efficient and work under low-data regimes; 2) adaptable, efficient, and quickto-train; 3) expressive and can handle complex TOD scenarios with multiple user intents in a single utterance.Motivated by these requirements, we introduce a novel framework for multi-label intent detection (mID): MULTI-CONVFIT (Multi-Label Intent Detection via Contrastive Conversational Fine-Tuning).While previous work on efficient single-label intent detection learns a classifier on top of a fixed sentence encoder (SE), we propose to 1) transform general-purpose SEs into task-specialized SEs via contrastive fine-tuning on annotated multi-label data, 2) where task specialization knowledge can be stored into lightweight adapter modules without updating the original parameters of the input SE, and then 3) we build improved mID classifiers stacked on top of fixed specialized SEs.Our main results indicate that MULTI-CONVFIT yields effective mID models, with large gains over non-specialized SEs reported across a spectrum of different mID datasets, both in low-data and high-data regimes.

Matthew Henderson, Ivan Vulić, Daniela Gerz, Iñigo Casanueva, Paweł Budzianowski, Sam Coope, Georgios Spithourakis, Tsung-Hsien Wen, Nikola Mrkšić, Pei-Hao Su. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019.

Numeracy is the ability to understand and work with numbers.It is a necessary skill for composing and understanding documents in clinical, scientific, and other technical domains.In this paper, we explore different strategies for modelling numerals with language models, such as memorisation and digit-by-digit composition, and propose a novel neural architecture that uses a continuous probability density function to model numerals from an open vocabulary.Our evaluation on clinical and scientific datasets shows that using hierarchical models to distinguish numerals from words improves a perplexity metric on the subset of numerals by 2 and 4 orders of magnitude, respectively, over nonhierarchical models.A combination of strategies can further improve perplexity.Our continuous probability density function model reduces mean absolute percentage errors by 18% and 54% in comparison to the second best strategy for each dataset, respectively.

The popularity of image sharing on social media and the engagement it creates between users reflect the important role that visual context plays in everyday conversations. We present a novel task, Image Grounded Conversations (IGC), in which natural-sounding conversations are generated about a shared image. To benchmark progress, we introduce a new multiple reference dataset of crowd-sourced, event-centric conversations on images. IGC falls on the continuum between chit-chat and goal-directed conversation models, where visual grounding constrains the topic of conversation to event-driven utterances. Experiments with models trained on social media data show that the combination of visual and textual context enhances the quality of generated conversational turns. In human evaluation, the gap between human performance and that of both neural and retrieval architectures suggests that multi-modal IGC presents an interesting challenge for dialog research.

Jiwei Li, Michel Galley, Chris Brockett, Georgios Spithourakis, Jianfeng Gao, Bill Dolan. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016.

Semantic error detection and correction is an important task for applications such as fact checking, speech-to-text or grammatical error correction.Current approaches generally focus on relatively shallow semantics and do not account for numeric quantities.Our approach uses language models grounded in numbers within the text.Such groundings are easily achieved for recurrent neural language model architectures, which can be further conditioned on incomplete background knowledge bases.Our evaluation on clinical reports shows that numerical grounding improves perplexity by 33% and F1 for semantic error correction by 5 points when compared to ungrounded approaches.Conditioning on a knowledge base yields further improvements.