DIALOG-ACT-TAGGING-FOR-CODE-MIXED-DATA-SET

In a task oriented domain, recognizing the intention of a speaker is important so that the conversation can proceed in the correct direction. This is possible only if there is a way to label the utterance with its proper intent. One such labeling technique is Dialog Act (DA) tagging. The main goal of this thesis is to build a Dialog Act tagger for the Telugu English Code Mixed corpus. Dialogue Act (DA) classification plays a key role in dialogue interpretation, especially in spontaneous conversation analysis. Dialogue acts are defined as the meaning of each utterance at the illocutionary force level. Code-Mixing (CM) is a very commonly observed mode of communication in a multilingual configuration. The trends of using this newly emerging language have its effect as a culling option especially in platforms like social media. This becomes particularly important in the context of technology and health, where expressing the upcoming advancements is difficult in native language. Despite the change of such language dynamics, current dialog systems cannot handle a switch between languages across sentences and mixing within a sentence. Everyday conversations are fabricated in this mixed language and analyzing dialog acts in this language is very essential in further advancements of making interaction with personal assistants more natural. Almost all standard traditional supervised machine learning approaches to classification have been applied in DA classification, from Support Vector Machines (SVM), Naïve Bayes, NLTK Classifiers, Max Entropy Classifier, Multilayer Perceptron, Conditional Random Field Classifier and Hidden Markov Model (HMM).