The number of people turning to the Internet to search for a diverse range of health-related subjects continues to grow and with this multitude of information available, duplicate questions become more frequent and finding the most appropriate answers becomes problematic. This issue is important for questionanswering platforms as it complicates the retrieval of all information relevant to the same topic, particularly when questions similar in essence are expressed differently, and answering a given medical question by retrieving similar questions that are already answered by human experts seems to be a promising solution. In this paper we present our novel approach to detect question entailment by determining the type of question asked rather than focusing on the type of the ailment given. This unique methodology makes the approach robust towards examples which have different ailment names but are synonyms of each other. Also it enables us to check entailment at a much more fine-grained level.
In this paper, we describe our system for COIN 2019 Shared Task 1: Commonsense Inference in Everyday Narrations Ostermann et al. (2019). We show the power of leveraging state-of-the-art pre-trained language models such as BERT (Bidirectional Encoder Representations from Transformers) Devlin et al. (2018) and XLNet Yang et al. (2019) over other Commonsense Knowledge Base Resources such as ConceptNet Speer et al. (2018) and NELL Mitchell et al. (2015) for modeling machine comprehension. We used an ensemble of BERT Large and XLNet Large. Experimental results show that our model gives substantial improvements over the baseline and other systems incorporating knowledge bases and got the 2nd position on the final test set leaderboard with an accuracy of 90.5%.
Although many pretrained models exist for text or images, there have been relatively fewer attempts to train representations specifically for dialog understanding. Prior works usually relied on finetuned representations based on generic text representation models like BERT or GPT-2. But such language modeling pretraining objectives do not take the structural information of conversational text into consideration. Although generative dialog models can learn structural features too, we argue that the structure-unaware word-by-word generation is not suitable for effective conversation modeling. We empirically demonstrate that such representations do not perform consistently across various dialog understanding tasks. Hence, we propose a structure-aware Mutual Information based loss-function DMI (Discourse Mutual Information) for training dialog-representation models, that additionally captures the inherent uncertainty in response prediction. Extensive evaluation on nine diverse dialog modeling tasks shows that our proposed DMI-based models outperform strong baselines by significant margins.
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