A Dataset for Building Code-Mixed Goal Oriented Conversation Systems

Proceedings of the 27th International Conference on Computational Linguistics, pages 3766–3780 Santa Fe, New Mexico, USA, August 20-26, 2018. 3766 A Dataset for Building Code-Mixed Goal Oriented Conversation Systems Suman Banerjee, Nikita Moghe, Siddhartha Arora and Indian Institute of Technology Madras, IndiaMitesh M. Khapra {suman, nikitavam, sidarora, miteshk}@cse.iitm.ac.in Abstract There is an increasing demand for goal-oriented conversation systems which can assist users in various day-to-day activities such as booking tickets, restaurant reservations, shopping, etc. Most of the existing datasets for building such conversation systems focus on monolingual con- versations and there is hardly any work on multilingual and/or code-mixed conversations. Such datasets and systems thus do not cater to the multilingual regions of the world, such as India, where it is very common for people to speak more than one language and seamlessly switch be- tween them resulting in code-mixed conversations. For example, a Hindi speaking user looking to book a restaurant would typically ask, “Kya tum is restaurant mein ek table book karne mein meri help karoge?” (“Can you help me in booking a table at this restaurant?”). To facilitate the development of such code-mixed conversation models, we build a goal-oriented dialog dataset containing code-mixed conversations. Specifically, we take the text from the DSTC2 restau- rant reservation dataset and create code-mixed versions of it in Hindi-English, Bengali-English, Gujarati-English and Tamil-English. We also establish initial baselines on this dataset using ex- isting state of the art models. This dataset along with our baseline implementations is made publicly available for research purposes. 1 Introduction Over the past few years, there has been an increasing demand for virtual assistants which can help users in a wide variety of tasks in several domains such as entertainment, finance, healthcare, e-commerce, etc. To cater to this demand, several commercial conversation systems such as Siri, Cortana, Allo have been developed. While these systems are still far from general purpose open domain chat, they perform reasonably well for certain goal-oriented tasks such as setting alarms/reminders, booking appointments, checking movie show timings, finding directions for navigation, etc. Apart from these commercial sys- tems, there has also been significant academic research to advance the state of the art in conversation systems (Shang et al., 2015; Vinyals and Le, 2015; Yao et al., 2015; Li et al., 2016a; Li et al., 2016b; Serban et al., 2017). Most of this academic research is driven by publicly available datasets such as Twitter conversation dataset (Ritter et al., 2010), Ubuntu dialog dataset (Lowe et al., 2015), Movie sub- titles dataset (Lison and Tiedemann, 2016) and DSTC2 restaurant reservation dataset (Henderson et al., 2014a). In this work, we focus on goal-oriented conversations such as the ones contained in the DSTC2 dataset. Most of the datasets and state of the art systems mentioned above are monolingual. Specifically, all the utterances and responses in the conversations are in one language (typically, English) and there are no multilingual and/or code-mixed utterances/responses. However, in several multilingual regions of the world, such as India, it is natural for speakers …

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