Alex Marin

“Can you give me another word for hyperbaric?”: Improving speech translation using targeted clarification questions

We present a novel approach for improving communication success between users of speech-to-speech translation systems by automatically detecting errors in the output of automatic speech recognition (ASR) and statistical machine translation (SMT) systems. Our approach initiates system-driven targeted clarification about errorful regions in user input and repairs them given user responses. Our system has been evaluated by unbiased subjects in live mode, and results show improved success of communication between users of the system.

Using syntactic and confusion network structure for out-of-vocabulary word detection

This paper addresses the problem of detecting words that are out-of-vocabulary (OOV) for a speech recognition system to improve automatic speech translation. The detection system leverages confidence prediction techniques given a confusion network representation and parsing with OOV word tokens to identify spans associated with true OOV words. Working in a resource-constrained domain, we achieve OOV detection F-scores of 60-66 and reduce word error rate by 12% relative to the case where OOV words are not detected.

Detecting authority bids in online discussions

This paper looks at the problem of detecting a particular type of social behavior in discussions: attempts to establish credibility as an authority on a particular topic. Using maximum entropy modeling, we explore questions related to feature extraction and turn vs. discussion-level modeling in experiments with online discussion text given only a small amount of labeled training data. We also introduce a method for learning interaction words from unlabeled data. Preliminary experiments show that a word-based approach (as used in topic classification) can be used successfully for turn-level modeling, but is less effective at the discussion level. We also find that sentence complexity features are almost as useful as lexical features, and that interaction words are more robust than the full vocabulary when combined with other features.

Learning Phrase Patterns for Text Classification

This paper introduces methods to discriminatively learn phrase patterns for use as features in text classification. An efficient solution is described using a recursive algorithm with a mutual information selection criterion. The algorithm automatically determines when word classes are useful in specific locations of a phrase pattern, allowing for variable specificity depending on the amount of labeled data available. Experiments are carried out on three text classification tasks in both English and Chinese, resulting in improved performance when adding the phrase patterns to the existing n-gram features.

Effective data-driven feature learning for detecting name errors in automatic speech recognition

This paper addresses the problem of detecting name errors in automatic speech recognition (ASR) output. The highly skewed label distributions (i.e. name errors are infrequent), sparse training data, and large number of potential lexical features pose significant challenges for training name error classification systems. Data-driven feature learning is needed for handling multiple languages but is sensitive to over fitting. We address the problem by designing aggregate features using a related (sentence-level name detection) task, and reduce dimensionality of the lexical features using word classes. Experiments on conversational domain data in both English and Iraqi Arabic show that best results are obtained using all feature mapping methods plus feature selection using L1 regularization.

Analyzing conversations using rich phrase patterns

Individual words are not powerful enough for many complex language classification problems. N-gram features include word context information, but are limited to contiguous word sequences. In this paper, we propose to use phrase patterns to extend n-grams for analyzing conversations, using a discriminative approach to learning patterns with a combination of words and word classes to address data sparsity issues. Improvements in performance are reported for two conversation analysis tasks: speaker role recognition and alignment classification.

Domain adaptation for parsing in automatic speech recognition

This paper addresses the problem of adapting a parser trained on out-of-domain data for use in automatic speech recognition (ASR) rescoring and error detection tasks. Using a self-training approach and adaptation with weakly-supervised data, we obtain improvements in ASR rescoring of confusion networks. Features extracted from the parser output are also used to improve detection of general ASR errors and out-of-vocabulary word regions in conjunction with a maximum entropy classifier.

Detecting targets of alignment moves in multiparty discussions

In analyzing goal-oriented multiparty discussions, one challenge is to determine who is responding to whom when they are supporting or opposing a remark put forward by another participant. This paper looks at algorithms for detecting the target discussant, comparing findings of important features for three genres of text and spoken discussions. Comparing to the common baseline of “previous speaker,” we find gains from considering content and semantic similarity in target detection, but with substantial differences in accuracy and important features across genres.