Unsupervised Dialogue Act Induction using Gaussian Mixtures

This paper introduces a new unsupervised approach for dialogue act induction. Given the sequence of dialogue utterances, the task is to assign them the labels representing their function in the dialogue. Utterances are represented as real-valued vectors encoding their meaning. We model the dialogue as Hidden Markov model with emission probabilities estimated by Gaussian mixtures. We use Gibbs sampling for posterior inference. We present the results on the standard Switchboard-DAMSL corpus. Our algorithm achieves promising results compared with strong supervised baselines and outperforms other unsupervised algorithms.Comment: Accepted to EACL 201

"How May I Help You?": Modeling Twitter Customer Service Conversations Using Fine-Grained Dialogue Acts

Given the increasing popularity of customer service dialogue on Twitter, analysis of conversation data is essential to understand trends in customer and agent behavior for the purpose of automating customer service interactions. In this work, we develop a novel taxonomy of fine-grained "dialogue acts" frequently observed in customer service, showcasing acts that are more suited to the domain than the more generic existing taxonomies. Using a sequential SVM-HMM model, we model conversation flow, predicting the dialogue act of a given turn in real-time. We characterize differences between customer and agent behavior in Twitter customer service conversations, and investigate the effect of testing our system on different customer service industries. Finally, we use a data-driven approach to predict important conversation outcomes: customer satisfaction, customer frustration, and overall problem resolution. We show that the type and location of certain dialogue acts in a conversation have a significant effect on the probability of desirable and undesirable outcomes, and present actionable rules based on our findings. The patterns and rules we derive can be used as guidelines for outcome-driven automated customer service platforms.Comment: 13 pages, 6 figures, IUI 201

Recognition of Dialogue Acts in the Estonian Dialogue Corpus: Overview of Resources and Software Development

Magistritöö eesmärgiks on kirjeldada Eesti dialoogikorpuse ressursside hetkeolukorda ja dialoogide märgendamiseks kasutatavaid vahendeid ning arendada edasi poolautomaatset märgendajat DAREC. Töös on kirjeldatud dialoogide ülesehitust, Eestis kasutatavat dialoogiaktide märgendamis-tüpoloogiat EDiT, samuti nii manuaalse kui ka automaatse märgendamistarkvara positiivseid ja negatiivseid külgi. 2007. aastal Mark Fišeli poolt loodud dialoogiaktide poolautomaatne märgendaja DAREC põhineb statistilisel meetodil. Esimeste testijate hinnangud olid küllaltki positiivsed seoses DARECi töö sisuliste tulemustega, kuna see kergendas oluliselt isegi väiksema täpsusega tuvastamise puhul õigete märgendite leidmist kuid negatiivsed seoses kasutajaliidesega. Viimasele heideti ette ebamugavust, ebapiisavat abiinfot, mõnede vajalike operatsioonide puudumist jms. Nende arvamuste põhjal kõrvaldati või leevendati käesoleva töö raames nimetatud puudusi, võttes aluseks heade kasutajaliideste loomise põhimõtted. Seejärel paluti dialoogide märgendajatel testida uut kasutajaliidest ning hinnangutest selgus, et süsteemi kasutajamugavus on olulisel määral kasvanud. Kõrgeimalt hinnati kasutajapärasust ja disaini ning kontekstitundlikku abiinfot, kuid samuti esitati erinevaid ideid süsteemi efektiivsemaks muutmiseks. Töös tuuakse ka võimalusi DARECi edasi¬arendamiseks: tuvastamistäpsuse ja saagise tõstmine algoritmi parandamise ja dialoogikorpuse suurenda¬mise läbi, ekspertvõimaluste lisamine jne.The aim of the thesis was to describe the present situation of the resources of the Estonian Dialogue Corpus and markup tools for dialogue acts as well as to develop the semi-automatic dialogue act markup tool DAREC. The thesis describes the structure of dialogue acts, the markup typology EdiT used in the Estonian Dialogue Corpus as well as the positive and negative sides of manual and automatic markup tools. The semi-automatic markup tool DAREC created by Mark Fishel in 2007 is based on a statistical method. Linguists’ first opinions were quite positive in terms of markup results. On the other hand, testers were critical about some features of the user interface, such as not beeing user-friendly, a poor manual, the absence of some important functions. Based on the users’ opinions and principles of creating good user interfaces most of the weaknesses were eliminated. The heuristic tests revealed that the usability of DAREC had remarkable improved. The most highly scored features included its user-friendliness, design and contextual help. At the same time various ideas for making the system more effective were suggested. The thesis also suggests several possibilities for developing DAREC, for example, increasing precision and recall of recognition by improving algorithm as well as the size of the dialogue corpus and adding more expert features

Social talk capabilities for dialogue systems

Small talk capabilities are an important but very challenging extension to dialogue systems. Small talk (or social talk) refers to a kind of conversation, which does not focus on the exchange of information, but on the negotiation of social roles and situations. The goal of this thesis is to provide knowledge, processes and structures that can be used by dialogue systems to satisfactorily participate in social conversations. For this purpose the thesis presents research in the areas of natural-language understanding, dialogue management and error handling. Nine new models of social talk based on a data analysis of small talk conversations are described. The functionally-motivated and content-abstract models can be used for small talk conversations on various topics. The basic elements of the models consist of dialogue acts for social talk newly developed on basis of social science theory. The thesis also presents some conversation strategies for the treatment of so-called out-of-domain (OoD) utterances that can be used to avoid errors in the input understanding of dialogue systems. Additionally, the thesis describes a new extension to dialogue management that flexibly manages interwoven dialogue threads. The small talk models as well as the strategies for handling OoD utterances are encoded as computational dialogue threads

Social talk capabilities for dialogue systems

Small talk capabilities are an important but very challenging extension to dialogue systems. Small talk (or “social talk”) refers to a kind of conversation, which does not focus on the exchange of information, but on the negotiation of social roles and situations. The goal of this thesis is to provide knowledge, processes and structures that can be used by dialogue systems to satisfactorily participate in social conversations. For this purpose the thesis presents research in the areas of natural-language understanding, dialogue management and error handling. Nine new models of social talk based on a data analysis of small talk conversations are described. The functionally-motivated and content-abstract models can be used for small talk conversations on various topics. The basic elements of the models consist of dialogue acts for social talk newly developed on basis of social science theory. The thesis also presents some conversation strategies for the treatment of so-called “out-of-domain” (OoD) utterances that can be used to avoid errors in the input understanding of dialogue systems. Additionally, the thesis describes a new extension to dialogue management that flexibly manages interwoven dialogue threads. The small talk models as well as the strategies for handling OoD utterances are encoded as computational dialogue threads