MidMed: Towards Mixed-Type Dialogues for Medical Consultation

Most medical dialogue systems assume that patients have clear goals (medicine querying, surgical operation querying, etc.) before medical consultation. However, in many real scenarios, due to the lack of medical knowledge, it is usually difficult for patients to determine clear goals with all necessary slots. In this paper, we identify this challenge as how to construct medical consultation dialogue systems to help patients clarify their goals. To mitigate this challenge, we propose a novel task and create a human-to-human mixed-type medical consultation dialogue corpus, termed MidMed, covering five dialogue types: task-oriented dialogue for diagnosis, recommendation, knowledge-grounded dialogue, QA, and chitchat. MidMed covers four departments (otorhinolaryngology, ophthalmology, skin, and digestive system), with 8,175 dialogues. Furthermore, we build baselines on MidMed and propose an instruction-guiding medical dialogue generation framework, termed InsMed, to address this task. Experimental results show the effectiveness of InsMed.

Multi-objective optimization via evolutionary algorithm (MOVEA) for high-definition transcranial electrical stimulation of the human brain

Transcranial temporal interference stimulation (tTIS) has been reported to be effective in stimulating deep brain structures in experimental studies. However, a computational framework for optimizing the tTIS strategy and simulating the impact of tTIS on the brain is still lacking, as previous methods rely on predefined parameters and hardly adapt to additional constraints. Here, we propose a general framework, namely multi-objective optimization via evolutionary algorithm (MOVEA), to solve the nonconvex optimization problem for various stimulation techniques, including tTIS and transcranial alternating current stimulation (tACS). By optimizing the electrode montage in a two-stage structure, MOVEA can be compatible with additional constraints (e.g., the number of electrodes, additional avoidance regions), and MOVEA can accelerate to obtain the Pareto fronts. These Pareto fronts consist of a set of optimal solutions under different requirements, suggesting a trade-off relationship between conflicting objectives, such as intensity and focality. Based on MOVEA, we make comprehensive comparisons between tACS and tTIS in terms of intensity, focality and maneuverability for targets of different depths. Our results show that although the tTIS can only obtain a relatively low maximum achievable electric field strength, for example, the maximum intensity of motor area under tTIS is 0.42V /m, while 0.51V /m under tACS, it helps improve the focality by reducing 60% activated volume outside the target. We further perform ANOVA on the stimulation results of eight subjects with tACS and tTIS. Despite the individual differences in head models, our results suggest that tACS has a greater intensity and tTIS has a higher focality. These findings provide guidance on the choice between tACS and tTIS and indicate a great potential in tTIS-based personalized neuromodulation. Code will be released soon.

Where to Go for the Holidays: Towards Mixed-Type Dialogs for Clarification of User Goals

Most dialog systems posit that users have figured out clear and specific goals before starting an interaction. For example, users have determined the departure, the destination, and the travel time for booking a flight. However, in many scenarios, limited by experience and knowledge, users may know what they need, but still struggle to figure out clear and specific goals by determining all the necessary slots. In this paper, we identify this challenge and make a step forward by collecting a new human-to-human mixed-type dialog corpus. It contains 5k dialog sessions and 168k utterances for 4 dialog types and 5 domains. Within each session, an agent first provides user-goal-related knowledge to help figure out clear and specific goals, and then help achieve them. Furthermore, we propose a mixed-type dialog model with a novel Prompt-based continual learning mechanism. Specifically, the mechanism enables the model to continually strengthen its ability on any specific type by utilizing existing dialog corpora effectively.

DuRecDial 2.0: A Bilingual Parallel Corpus for Conversational Recommendation

In this paper, we provide a bilingual parallel human-to-human recommendation dialog dataset (DuRecDial 2.0) to enable researchers to explore a challenging task of multilingual and cross-lingual conversational recommendation. The difference between DuRecDial 2.0 and existing conversational recommendation datasets is that the data item (Profile, Goal, Knowledge, Context, Response) in DuRecDial 2.0 is annotated in two languages, both English and Chinese, while other datasets are built with the setting of a single language. We collect 8.2k dialogs aligned across English and Chinese languages (16.5k dialogs and 255k utterances in total) that are annotated by crowdsourced workers with strict quality control procedure. We then build monolingual, multilingual, and cross-lingual conversational recommendation baselines on DuRecDial 2.0. Experiment results show that the use of additional English data can bring performance improvement for Chinese conversational recommendation, indicating the benefits of DuRecDial 2.0. Finally, this dataset provides a challenging testbed for future studies of monolingual, multilingual, and cross-lingual conversational recommendation.

Towards Conversational Recommendation over Multi-Type Dialogs

We propose a new task of conversational recommendation over multi-type dialogs, where the bots can proactively and naturally lead a conversation from a non-recommendation dialog (e.g., QA) to a recommendation dialog, taking into account user's interests and feedback. To facilitate the study of this task, we create a human-to-human Chinese dialog dataset \emph{DuRecDial} (about 10k dialogs, 156k utterances), which contains multiple sequential dialogs for every pair of a recommendation seeker (user) and a recommender (bot). In each dialog, the recommender proactively leads a multi-type dialog to approach recommendation targets and then makes multiple recommendations with rich interaction behavior. This dataset allows us to systematically investigate different parts of the overall problem, e.g., how to naturally lead a dialog, how to interact with users for recommendation. Finally we establish baseline results on DuRecDial for future studies. Dataset and codes are publicly available at https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/Research/ACL2020-DuRecDial.