An Active Learning Approach to the Classification of Non-Sentential Utterances
Authors: Paolo Dragone, Pierre Lison (Sapienza Università di Roma, University of Oslo, 2015)
Topic: Multimodal

Abstract
This paper addresses the problem of classification of non-sentential utterances (NSUs). NSUs are utterances that do not have a complete sentential form but convey a full clausal meaning given the dialogue context. We extend the approach of Fernández et al. (2007), which provide a taxonomy of NSUs and a small annotated corpus extracted from dialogue transcripts. This paper demonstrates how the combination of new linguistic features and active learning techniques can mitigate the scarcity of labelled data. The results show a significant improvement in the classification accuracy over the state-of-the-art

Introduction
In dialogue, utterances do not always take the form of complete, well-formed sentences with a subject, a verb and complements. Many utterances – often called non-sentential utterances, or NSUs for short – are fragmentary and lack an overt predicate. Consider the following examples from the British National Corpus: A: How do you actually feel about that? B: Not too happy. [BNC: JK8 168-169] A: They wouldn’t do it, no. B: Why? [BNC: H5H 202-203] A: [...] then across from there to there. B: From side to side. [BNC: HDH 377-378] Despite their ubiquity, the semantic content of NSUs is often difficult to extract automatically. Non-sentential utterances are indeed intrinsically dependent on the dialogue context for their interpretation – for instance, the meaning of ”why” in the example above is impossible to decipher without knowing what precedes it. This paper describes a new approach to the classification of NSUs. The approach builds upon the work of Fernández et al. (2007), which present a corpus of NSUs along with a taxonomy and a classifier based on simple features. In particular, we show that the inclusion of new linguistic features and the use of active learning provide a modest but significant improvement in the classification accuracy compared to their approach. The next section presents the corpus used in this work and its associated taxonomy of NSUs. Section 3 describes our classification approach (extracted features and learning algorithm). Section 4 finally presents the empirical results and their comparison with the baseline.

Conclusion
This paper presented the results of an experiment in the classification of non-sentential utterances, extending the work of Fernández et al. (2007). The approach relied on an extended feature set and active learning techniques to address the scarcity of labelled data and the class imbalance. The evaluation results demonstrated a significant improvement in the classification accuracy. The presented results also highlight the need for a larger annotated corpus of NSUs. In our view, the development of such a corpus, including new dialogue domains and a broader range of conversational phenomena, could contribute to a better understanding of NSUs and their interpretation. Furthermore, the classification of NSUs according to their type only constitutes the first step in their semantic interpretation. Dragone and Lison (2015) focuses on integrating the NSU classification outputs for natural language understanding of conversational data, building upon Ginzburg (2012)’s formal theory of conversation.