SQL Comment Generation and Additional Research Interests (2023)

Abstract: My research interests focus on natural language generation (NLG) regarding how to make system outputs more intuitive and comprehensible for the human-user and conversational entrainment and alignment from the perspective of how dialogue systems could or should personalize its responses to the human user. As it relates to NLG, my current work focuses on training a system to auto-generate comments for SQL queries produced by a Text-to-SQL parser. The goal is to make the connection between technical SQL language and the user’s question more transparent. My linguistic training lies primarily at the intersection of computational and socio-linguistics. As such, my curiosities in conversational entrainment and alignment focus on the extent to which conversational agents can or should adjust their language based on human characteristics such as age, race, or gender.

## research interests my research interests focus on natural language generation (nlg) regarding how to make system outputs more intuitive and comprehensible for the human-user and conversational entrainment and alignment from the perspective of how dialogue systems could or should personalize its responses to the human user. as it relates to nlg, my current work focuses on training a system to auto-generate comments for sql queries produced by a text-to-sql parser. the goal is to make the connection between technical sql language and the user's question more transparent. my linguistic training lies primarily at the intersection of computational and sociolinguistics. as such, my curiosities in conversational entrainment and alignment focus on the extent to which conversational agents can or should adjust their language based on human characteristics such as age, race, gender, etc. ## spoken dialogue system (sds) research sds research will continue to dive into questions around interpretability, explainability, and controllability as llm capabilities progress. questions around ethical usage of using conversational agents and improving robot social intelligence will also continue to be major considerations in the field. i think a growing interest will be in the dynamics occurring between the human and the dialogue system during a conversation. as conversational agents become even more commonplace in the coming years, understanding the machine as an active interlocutor will be necessary in order to create more advanced conversational experiences for humans. i am curious about what socially-focused considerations could result in improved levels of personalization versus what adjustments could lead to toxic or harmful speech. for example, one could imagine a dialogue system trained to speak to all women in one way and all men in another would lead to harmful stereotyping. improved levels of personalization though could take shape as a system picking up on a human user's dialect and leveraging words from that dialect to appear more familiar. when considering future applications of sds, it will be crucial for research in academic and industry spaces to be discussed and available between the groups. as products are being developed and launched in industry, findings from academia can be helpful in improving the way these systems are designed. i think this collaboration is where questions around what should a system be capable of in addition to what the system can do will be most effectively addressed.