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We investigate unsupervised techniques for acquiring monolingual sentence-level paraphrases from a corpus of temporally and topically clustered news articles collected from thousands of web-based news sources. Two techniques are employed: (1) simple string edit distance, and (2) a heuristic strategy that pairs initial (presumably summary) sentences from(More)
We apply statistical machine translation (SMT) tools to generate novel paraphrases of input sentences in the same language. The system is trained on large volumes of sentence pairs automatically extracted from clustered news articles available on the World Wide Web. Alignment Error Rate (AER) is measured to gauge the quality of the resulting corpus. A(More)
We present a modular system for detection and correction of errors made by non-native (English as a Second Language = ESL) writers. We focus on two error types: the incorrect use of determiners and the choice of prepositions. We use a decision-tree approach inspired by contextual spelling systems for detection and correction suggestions, and a large(More)
This paper presents a pilot study of the use of phrasal Statistical Machine Translation (SMT) techniques to identify and correct writing errors made by learners of English as a Second Language (ESL). Using examples of mass noun errors found in the Chinese Learner Error Corpus (CLEC) to guide creation of an engineered training set, we show that application(More)
We present a novel response generation system that can be trained end to end on large quantities of unstructured Twitter conversations. A neural network architecture is used to address sparsity issues that arise when integrating contextual information into classic statistical models, allowing the system to take into account previous dialog utterances. Our(More)
In recent years, there has been increased interest in topic-focused multi-document summarization. In this task, automatic summaries are produced in response to a specific information request, or topic, stated by the user. The system we have designed to accomplish this task comprises four main components: a generic extractive summarization system, a(More)
We present a machine learning approach to evaluating the well-formedness of output of a machine translation system, using classifiers that learn to distinguish human reference translations from machine translations. This approach can be used to evaluate an MT system, tracking improvements over time; to aid in the kind of failure analysis that can help guide(More)
This paper describes a method of extracting katakana words and phrases, along with their English counterparts from non-aligned monolingual web search engine query logs. The method employs a trainable edit distance function to find <katakana, English> pairs that have a high probability of being equivalent. These pairs can then be used to further bootstrap(More)
The lack of readily-available large corpora of aligned monolingual sentence pairs is a major obstacle to the development of Statistical Machine Translation based paraphrase models. In this paper, we describe the use of annotated datasets and Support Vector Machines to induce larger monolingual paraphrase corpora from a comparable corpus of news clusters(More)