Beyond topical relevance, passage ranking for open-domain factoid question answering also requires a passage to contain an answer (answerability).
Deep Q-learning algorithms often suffer from poor gradient estimations with an excessive variance, resulting in unstable training and poor sampling efficiency.
This paper addresses the task of query-focused video summarization, which takes user's query and a long video as inputs and aims to generate a query-focused video summary.
To improve the quality and retrieval performance of the generated questions, we make two major improvements: 1) To better encode the semantics of ill-formed questions, we enrich the representation of questions with character embedding and the recent proposed contextual word embedding such as BERT, besides the traditional context-free word embeddings; 2) To make it capable to generate desired questions, we train the model with deep reinforcement learning techniques that considers an appropriate wording of the generation as an immediate reward and the correlation between generated question and answer as time-delayed long-term rewards.
However, one critical problem is that current approaches only get high accuracy for questions whose relations have been seen in the training data.
Abstract Meaning Representation (AMR) parsing aims at abstracting away from the syntactic realization of a sentence, and denote only its meaning in a canonical form.