If You Can't Beat Them Join Them: Handcrafted Features Complement Neural Nets for Non-Factoid Answer Reranking

If you can't beat them, join them: the University of Alberta system description

We describe our approach and experiments in the context of the CoNLLSIGMORPHON 2017 Shared Task on Universal Morphological Reinflection. We combine a discriminative transduction system with neural models. The results on five languages show that our approach works well in the low-resource setting. We also investigate adaptations designed to handle small training sets.

Discourse Complements Lexical Semantics for Non-factoid Answer Reranking

We propose a robust answer reranking model for non-factoid questions that integrates lexical semantics with discourse information, driven by two representations of discourse: a shallow representation centered around discourse markers, and a deep one based on Rhetorical Structure Theory. We evaluate the proposed model on two corpora from different genres and domains: one from Yahoo! Answers and ...

If You Can't Beat 'Em, Join 'Em: A Pragmatic Approach to Nonprecedential Opinions in the Federal Appellate Courts

Beyond Factoid QA: Effective Methods for Non-factoid Answer Sentence Retrieval

Retrieving finer grained text units such as passages or sentences as answers for non-factoid Web queries is becoming increasingly important for applications such as mobile Web search. In this work, we introduce the answer sentence retrieval task for non-factoid Web queries, and investigate how this task can be effectively solved under a learning to rank framework. We design two types of feature...

This is how we do it: Answer Reranking for Open-domain How Questions with Paragraph Vectors and Minimal Feature Engineering

We present a simple yet powerful approach to non-factoid answer reranking whereby question-answer pairs are represented by concatenated distributed representation vectors and a multilayer perceptron is used to compute the score for an answer. Despite its simplicity, our approach achieves state-of-the-art performance on a public dataset of How questions, outperforming systems which employ sophis...