Convolutional-Match Networks for Question Answering

In this paper, we present a simple, yet effective, attention and memory mechanism that is reminis- cent of Memory Networks and we demonstrate it in question-answering scenarios. Our mechanism is based on four simple premises: a) memories can be formed from word sequences by using convo- lutional networks; b) distance measurements can be taken at a neuronal level; c) a recursive soft- max function can be used for attention; d) extensive weight sharing can help profoundly. We achieve state-of-the-art results in the bAbI tasks, outper- forming Memory Networks and the Differentiable Neural Computer, both in terms of accuracy and stability (i.e. variance) of results

Neural Architecture for Question Answering Using a Knowledge Graph and Web Corpus

In Web search, entity-seeking queries often trigger a special Question Answering (QA) system. It may use a parser to interpret the question to a structured query, execute that on a knowledge graph (KG), and return direct entity responses. QA systems based on precise parsing tend to be brittle: minor syntax variations may dramatically change the response. Moreover, KG coverage is patchy. At the other extreme, a large corpus may provide broader coverage, but in an unstructured, unreliable form. We present AQQUCN, a QA system that gracefully combines KG and corpus evidence. AQQUCN accepts a broad spectrum of query syntax, between well-formed questions to short `telegraphic' keyword sequences. In the face of inherent query ambiguities, AQQUCN aggregates signals from KGs and large corpora to directly rank KG entities, rather than commit to one semantic interpretation of the query. AQQUCN models the ideal interpretation as an unobservable or latent variable. Interpretations and candidate entity responses are scored as pairs, by combining signals from multiple convolutional networks that operate collectively on the query, KG and corpus. On four public query workloads, amounting to over 8,000 queries with diverse query syntax, we see 5--16% absolute improvement in mean average precision (MAP), compared to the entity ranking performance of recent systems. Our system is also competitive at entity set retrieval, almost doubling F1 scores for challenging short queries.Comment: Accepted to Information Retrieval Journa