Articles

We present a probabilistic approach for learn- ing to interpret temporal phrases given only a corpus of utterances and the times they ref- erence. While most approaches to the task have used regular expressions and similar lin- ear pattern interpretation rules, the possibil- ity of phrasal embedding and modification in …...

In a conventional telephone conversation be- tween two speakers of the same language, the interaction is real-time and the speakers pro- cess the information stream incrementally. In this work, we address the problem of incre- mental speech-to-speech translation (S2S) that enables cross-lingual communication between two remote participants over a telephone. …...

There has been a proliferation of recent work on SMT tuning algorithms capable of han- dling larger feature sets than the traditional MERT approach. We analyze a number of these algorithms in terms of their sentence- level loss functions, which motivates several new approaches, including a Structured SVM. We perform …...

It has long been observed that monolingual text exhibits a tendency toward “one sense per dis- course,” and it has been argued that a related “one translation per discourse” constraint is op- erative in bilingual contexts as well. In this pa- per, we introduce a novel method using forced decoding …...

Sequential transduction tasks, such as grapheme-to-phoneme conversion and ma- chine transliteration, are usually addressed by inducing models from sets of input-output pairs. Supplemental representations offer valu- able additional information, but incorporating that information is not straightforward. We apply a unified reranking approach to both grapheme-to-phoneme conversion and ma- chine transliteration …...

This paper presents a novel approach for in- ducing lexical taxonomies automatically from text. We recast the learning problem as that of inferring a hierarchy from a graph whose nodes represent taxonomic terms and edges their degree of relatedness. Our model takes this graph representation as input and fits a …...

We investigate a language model that com- bines morphological and shape features with a Kneser-Ney model and test it in a large crosslingual study of European languages. Even though the model is generic and we use the same architecture and features for all lan- guages, the model achieves reductions in …...

We report on a pilot experiment to improve the per- formance of an automatic speech recognizer (ASR) by using a single-channel EEG signal to classify the speaker’s mental state as reading easy or hard text. We use a previously published method (Mostow et al., 2011) to train the EEG classifier. …...

This paper proposes an improved approach to extrac- tive summarization of spoken multi-party interac- tion, in which integrated random walk is performed on a graph constructed on topical/ lexical relations. Each utterance is represented as a node of the graph, and the edges’ weights are computed from the topi- cal …...

We evaluate the performance of an morpho- logical analyser for Inuktitut across a medium- sized corpus, where it produces a useful anal- ysis for two out of every three types. We then compare its segmentation to that of sim- pler approaches to morphology, and use these as a pre-processing step …...

Motivated by the fact that the pronuncia- tion of a name may be influenced by its language of origin, we present methods to improve pronunciation prediction of proper names using word origin information. We train grapheme-to-phoneme (G2P) models on language-specific data sets and interpolate the outputs. We perform experiments on …...

In this paper we present a fully unsupervised nonparametric Bayesian model that jointly in- duces POS tags and morphological segmen- tations. The model is essentially an infi- nite HMM that infers the number of states from data. Incorporating segmentation into the same model provides the morphological features to the system …...

We introduce a new segmentation evaluation measure, WinPR, which resolves some of the limitations of WindowDiff. WinPR distin- guishes between false positive and false nega- tive errors; produces more intuitive measures, such as precision, recall, and F-measure; is in- sensitive to window size, which allows us to customize near miss …...

We present a classifier that discriminates be- tween types of corrections made by teachers of English in student essays. We define a set of linguistically motivated feature templates for a log-linear classification model, train this classifier on sentence pairs extracted from the Cambridge Learner Corpus, and achieve 89% accuracy improving …...

First story detection (FSD) involves identify- ing first stories about events from a continuous stream of documents. A major problem in this task is the high degree of lexical variation in documents which makes it very difficult to de- tect stories that talk about the same event but expressed using …...

We propose a linguistically motivated set of features to capture morphological agreement and add them to the MSTParser dependency parser. Compared to the built-in morphologi- cal feature set, ours is both much smaller and more accurate across a sample of 20 morpho- logically annotated treebanks. We find in- creases in …...

We investigate the problem of automatically converting from a dependency representa- tion to a phrase structure representation, a key aspect of understanding the relationship between these two representations for NLP work. We implement a new approach to this problem, based on a small number of su- pertags, along with an …...

We apply Combinatory Categorial Grammar to wide-coverage parsing in Chinese with the new Chinese CCGbank, bringing a formalism capable of transparently recovering non-local dependencies to a language in which they are particularly frequent. We train two state-of-the-art English  parsers: the parser of Petrov and Klein (P&K), and the Clark …...

We introduce a method for learning to predict text completion given a source text and partial translation. In our approach, predictions are offered aimed at alleviating users’ burden on lexical and grammar choices, and improving productivity. The method involves learning syntax-based phraseology and translation equivalents. At run-time, the source and …...

While the field of grammatical error detection has progressed over the past few years, one area of particular difficulty for both native and non-native learners of English, comma place- ment, has been largely ignored. We present a system for comma error correction in English that achieves an average of 89% …...