Correcting Keyboard Layout Errors and Homoglyphs in Queries

Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Correcting Keyboard Layout Errors and Homoglyphs in Queries
Abstract

Keyboard layout errors and homoglyphs in cross-language queries impact our ability to correctly interpret user information needs and offer relevant results. We present a machine learning approach to correcting these errors, based largely on character-level n-gram features. We demonstrate superior performance over rule-based methods, as well as a significant reduction in the number of queries that yield null search results.

Another publication from the same author:

40th International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2015

Switching to and Combining Offline-Adapted Cluster Acoustic Models based on Unsupervised Segment Classification

The performance of automatic speech recognition system degrades significantly when the incoming audio differs from training data. Maximum likelihood linear regression has been widely used for unsupervised adaptation, usually in a multiple-pass recognition process. Here we present a novel adaptation framework for which the offline, supervised, high-quality adaptation is applied to clustered channel/speaker conditions that are defined with automatic and manual clustering of the training data. Upon online recognition, each speech segment is classified into one of the training clusters in an unsupervised way, and the corresponding top acoustic models are used for recognition. Recognition lattice outputs are combined. Experiments are performed on the Wall Street Journal data, and a 37.5% relative reduction of Word Error Rate is reported. The proposed approach is also compared with a general speaker adaptive training approach.

Another publication from the same category: Machine Translation

MT Summit, Nagoya, Japan, September 2017

Harvesting Polysemous Terms from e-commerce Data to Enhance QA

Silvio Picinini

Polysemous words can be difficult to translate and can affect the quality of Machine Translation (MT) output. Once the MT quality is affected, it has a direct impact on post-editing and on human-assisted machine translation. The presence of these terms increases the risk of errors. We think that these important words can be used to improve and to measure quality of translations. We present three methods for finding these words from e-commerce data, based on Named Entity Recognition, Part-of-Speech and Search Queries.