Recommender systems exploit interaction history to estimate user preference, having been
heavily used in a wide range of industry applications. However, static recommendation …

The field of machine learning is witnessing its golden era as deep learning slowly becomes
the leader in this domain. Deep learning uses multiple layers to represent the abstractions of …

This paper proposes a new model for extracting an interpretable sentence embedding by
introducing self-attention. Instead of using a vector, we use a 2-D matrix to represent the …

To implement a program functionality, developers can reuse previously written code
snippets by searching through a large-scale codebase. Over the years, many code search …

Ranking models lie at the heart of research on information retrieval (IR). During the past
decades, different techniques have been proposed for constructing ranking models, from …

This paper provides a unified account of two schools of thinking in information retrieval
modelling: the generative retrieval focusing on predicting relevant documents given a query …

How to model a pair of sentences is a critical issue in many NLP tasks such as answer
selection (AS), paraphrase identification (PI) and textual entailment (TE). Most prior work (i) …

Privacy policies are the primary channel through which companies inform users about their
data collection and sharing practices. These policies are often long and difficult to …

In this paper, we apply a general deep learning (DL) framework for the answer selection
task, which does not depend on manually defined features or linguistic tools. The basic …

We describe SemEval-2017 Task 3 on Community Question Answering. This year, we reran
the four subtasks from SemEval-2016:(A) Question-Comment Similarity,(B) Question …