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: Hassan Sawaf

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

Association for Machine Translation in the Americas (AMTA), Oct. 2016

Guided Alignment Training for Topic-Aware Neural Machine Translation

Wenhu Chen, Evgeny Matusov, Shahram Khadivi, Jan-Thorsten Peter

In this paper, we propose an effective way for biasing the attention mechanism of a sequence-to-sequence neural machine translation (NMT) model towards the well-studied statistical word alignment models. We show that our novel guided alignment training approach improves translation quality on real-life e-commerce texts consisting of product titles and descriptions, overcoming the problems posed by many unknown words and a large type/token ratio. We also show that meta-data associated with input texts such as topic or category information can significantly improve translation quality when used as an additional signal to the decoder part of the network. With both novel features, the BLEU score of the NMT system on a product title set improves from 18.6 to 21.3%. Even larger MT quality gains are obtained through domain adaptation of a general domain NMT system to e-commerce data. The developed NMT system also performs well on the IWSLT speech translation task, where an ensemble of four variant systems outperforms the phrase-based baseline by 2.1% BLEU absolute.