488 research outputs found
Deep Short Text Classification with Knowledge Powered Attention
Short text classification is one of important tasks in Natural Language
Processing (NLP). Unlike paragraphs or documents, short texts are more
ambiguous since they have not enough contextual information, which poses a
great challenge for classification. In this paper, we retrieve knowledge from
external knowledge source to enhance the semantic representation of short
texts. We take conceptual information as a kind of knowledge and incorporate it
into deep neural networks. For the purpose of measuring the importance of
knowledge, we introduce attention mechanisms and propose deep Short Text
Classification with Knowledge powered Attention (STCKA). We utilize Concept
towards Short Text (C- ST) attention and Concept towards Concept Set (C-CS)
attention to acquire the weight of concepts from two aspects. And we classify a
short text with the help of conceptual information. Unlike traditional
approaches, our model acts like a human being who has intrinsic ability to make
decisions based on observation (i.e., training data for machines) and pays more
attention to important knowledge. We also conduct extensive experiments on four
public datasets for different tasks. The experimental results and case studies
show that our model outperforms the state-of-the-art methods, justifying the
effectiveness of knowledge powered attention
Wronging a Right: Generating Better Errors to Improve Grammatical Error Detection
Grammatical error correction, like other machine learning tasks, greatly
benefits from large quantities of high quality training data, which is
typically expensive to produce. While writing a program to automatically
generate realistic grammatical errors would be difficult, one could learn the
distribution of naturallyoccurring errors and attempt to introduce them into
other datasets. Initial work on inducing errors in this way using statistical
machine translation has shown promise; we investigate cheaply constructing
synthetic samples, given a small corpus of human-annotated data, using an
off-the-rack attentive sequence-to-sequence model and a straight-forward
post-processing procedure. Our approach yields error-filled artificial data
that helps a vanilla bi-directional LSTM to outperform the previous state of
the art at grammatical error detection, and a previously introduced model to
gain further improvements of over 5% score. When attempting to
determine if a given sentence is synthetic, a human annotator at best achieves
39.39 score, indicating that our model generates mostly human-like
instances.Comment: Accepted as a short paper at EMNLP 201
Mining Implicit Relevance Feedback from User Behavior for Web Question Answering
Training and refreshing a web-scale Question Answering (QA) system for a
multi-lingual commercial search engine often requires a huge amount of training
examples. One principled idea is to mine implicit relevance feedback from user
behavior recorded in search engine logs. All previous works on mining implicit
relevance feedback target at relevance of web documents rather than passages.
Due to several unique characteristics of QA tasks, the existing user behavior
models for web documents cannot be applied to infer passage relevance. In this
paper, we make the first study to explore the correlation between user behavior
and passage relevance, and propose a novel approach for mining training data
for Web QA. We conduct extensive experiments on four test datasets and the
results show our approach significantly improves the accuracy of passage
ranking without extra human labeled data. In practice, this work has proved
effective to substantially reduce the human labeling cost for the QA service in
a global commercial search engine, especially for languages with low resources.
Our techniques have been deployed in multi-language services.Comment: Accepted by KDD 202
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