We study pragmatics in political campaign text, through analysis of speech acts and the target of each utterance. We propose a new annotation schema incorporating domain-specific speech acts, such as commissive-action, and present a novel annotated corpus of media releases and speech transcripts from the 2016 Australian election cycle. We show how speech acts and target referents can be modeled as sequential classification, and evaluate several techniques, exploiting contextualized word representations, semi-supervised learning, task dependencies and speaker meta-data.
Speech act theory is fundamental to study such discourse and pragmatics @cite_13 @cite_26 . A speech act is an illocutionary act of conversation and reflects shallow discourse structures of language. Due to its predominantly small-data setting, speech act classification approaches have generally relied on bag-of-words models @cite_24 @cite_9 , although recent approaches have used deep-learning models through data augmentation @cite_14 and learning word representations for the target domain @cite_23 , outperforming traditional bag-of-words approaches.
@cite_14 @cite_26 @cite_9 @cite_24 @cite_23 @cite_13
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Abstract This paper argues for a reorientation of speech act theory towards an Austin-inspired conception of speech acts as context-changing social actions. After an overview of the role assigned to context by Austin, Searle, and other authors in pragmatics, it is argued that the context of a speech act should be considered as constructed as opposed to merely given, limited as opposed to extensible in any direction, and objective as opposed to cognitive. The compatibility of such claims with each other is discussed. Finally, the context-changing role of speech acts is analyzed differentiating between the illocutionary and the perlocutionary dimension. This paper presents a series of experiments in applying compositional distributional semantic models to dialogue act classification. In contrast to the widely used bag-ofwords approach, we build the meaning of an utterance from its parts by composing the distributional word vectors using vector addition and multiplication. We investigate the contribution of word sequence, dialogue act sequence, and distributional information to the performance, and compare with the current state of the art approaches. Our experiment suggests that that distributional information is useful for dialogue act tagging but that simple models of compositionality fail to capture crucial information from word and utterance sequence; more advanced approaches (e.g. sequence- or grammar-driven, such as categorical, word vector composition) are required. Previous work on dialogue act classification have primarily focused on dense generative and discriminative models. However, since the automatic speech recognition (ASR) outputs are often noisy, dense models might generate biased estimates and overfit to the training data. In this paper, we study sparse modeling approaches to improve dialogue act classification, since the sparse models maintain a compact feature space, which is robust to noise. To test this, we investigate various element-wise frequentist shrinkage models such as lasso, ridge, and elastic net, as well as structured sparsity models and a hierarchical sparsity model that embed the dependency structure and interaction among local features. In our experiments on a real-world dataset, when augmenting N-best word and phone level ASR hypotheses with confusion network features, our best sparse log-linear model obtains a relative improvement of 19.7 over a rule-based baseline, a 3.7 significant improvement over a traditional non-sparse log-linear model, and outperforms a state-of-the-art SVM model by 2.2 . We describe a statistical approach for modeling dialogue acts in conversational speech, i.e., speech-act-like units such as STATEMENT, QUESTION, BACKCHANNEL, AGREEMENT, DISAGREEMENT, and APOLOGY. Our model detects and predicts dialogue acts based on lexical, collocational, and prosodic cues, as well as on the discourse coherence of the dialogue act sequence. The dialogue model is based on treating the discourse structure of a conversation as a hidden Markov model and the individual dialogue acts as observations emanating from the model states. Constraints on the likely sequence of dialogue acts are modeled via a dialogue act n-gram. The statistical dialogue grammar is combined with word n-grams, decision trees, and neural networks modeling the idiosyncratic lexical and prosodic manifestations of each dialogue act. We develop a probabilistic integration of speech recognition with dialogue modeling, to improve both speech recognition and dialogue act classification accuracy. Models are trained and evaluated using a large hand-labeled database of 1,155 conversations from the Switchboard corpus of spontaneous human-to-human telephone speech. We achieved good dialogue act labeling accuracy (65 based on errorful, automatically recognized words and prosody, and 71 based on word transcripts, compared to a chance baseline accuracy of 35 and human accuracy of 84 ) and a small reduction in word recognition error.