Articles

Search engines are increasingly relying on large knowledge bases of facts to provide direct answers to users’ queries. How- ever, the construction of these knowledge bases is largely manual and does not scale to the long and heavy tail of facts. Open information extraction tries to address this challenge, but …...

We propose the first probabilistic approach to modeling cross-lingual semantic sim- ilarity (CLSS) in context which requires only comparable data. The approach re- lies on an idea of projecting words and sets of words into a shared latent semantic space spanned by language-pair indepen- dent latent semantic concepts (e.g., cross- …...

Child semantic development includes learning the meaning of words as well as the semantic relations among words. presumed outcome of semantic develop-A ment is the formation of a semantic net- work that reflects this knowledge. We present an algorithm for simultaneously learning word meanings and gradually growing a semantic network, …...

In this paper, we present a novel exten- sion of a forest-to-string machine transla- tion system with a reordering model. We predict reordering probabilities for every pair of source words with a model using features observed from the input parse for- est. Our approach naturally deals with the ambiguity present …...

Left-to-right (LR) decoding (Watanabe et al., 2006) is promising decoding algorithm for hierarchical phrase-based translation (Hiero) that visits input spans in arbitrary order producing the output translation in left to right order. This leads to far fewer language model calls, but while LR decod- ing is more efficient than CKY …...

State-of-art systems for grammar error correction often correct errors based on word sequences or phrases. In this paper, we describe a grammar error correction system which corrects grammatical errors at tree level directly. We cluster all error into two groups and divide our system into two modules correspondingly: the general …...

We present a pairwise learning-to-rank approach to machine translation evalua- tion that learns to differentiate better from worse translations in the context of a given reference. We integrate several layers of linguistic information encapsulated in tree-based structures, making use of both the reference and the system output simul- taneously, thus …...

We present a human judgments dataset and an adapted metric for evaluation of Arabic machine translation. Our medium- scale dataset is the first of its kind for Ara- bic with high annotation quality. We use the dataset to adapt the BLEU score for Arabic. Our score (AL-BLEU) provides partial credits …...

This article describes a linguistically in- formed method for integrating phrasal verbs into statistical machine translation (SMT) systems. In a case study involving English to Bulgarian SMT, we show that our method does not only improve trans- lation quality but also outperforms simi- lar methods previously applied to the same …...

Since larger n-gram Language Model (LM) usually performs better in Statistical Machine Translation (SMT), how to con- struct efficient large LM is an important topic in SMT. However, most of the ex- isting LM growing methods need an extra monolingual corpus, where additional LM adaption technology is necessary. In this …...

We propose a simple and effective ap- proach to learn translation spans for the hierarchical phrase-based translation model. Our model evaluates if a source span should be covered by translation rules during decoding, which is integrated into the translation system as soft con- straints. Compared to syntactic con- straints, our …...

Sentence level evaluation in MT has turned out far more difficult than corpus level evaluation. Existing sentence level metrics employ a lim- ited set of features, most of which are rather sparse at the sentence level, and their intricate models are rarely trained for ranking. This pa- per presents a …...

We study a novel architecture for syntactic SMT. In contrast to the dominant approach in the literature, the system does not rely on translation rules, but treat translation as an unconstrained target sentence gen- eration task, using soft features to cap- ture lexical and syntactic correspondences between the source and …...

Recently, syntactic information has helped significantly to improve statistical ma- chine translation. However, the use of syn- tactic information may have a negative im- pact on the speed of translation because of the large number of rules, especially when syntax labels are projected from a parser in syntax-augmented machine translation. …...

Generative word alignment models, such as IBM Models, are restricted to one- to-many alignment, and cannot explicitly represent many-to-many relationships in a bilingual text. The problem is par- tially solved either by introducing heuris- tics or by agreement constraints such that two directional word alignments agree with each other. In …...

We present a novel approach to im- prove word alignment for statistical ma- chine translation (SMT). Conventional word alignment methods allow discontin- uous alignment, meaning that a source (or target) word links to several target (or source) words whose positions are dis- continuous. However, we cannot extrac- t phrase pairs …...

We investigate how to improve bilingual embedding which has been successfully used as a feature in phrase-based sta- tistical machine translation (SMT). De- spite bilingual embedding’s success, the contextual information, which is of criti- cal importance to translation quality, was ignored in previous work. To employ the contextual information, we …...

We introduce submodular optimization to the problem of training data subset selection for statistical machine translation (SMT). By explicitly formulating data selection as a submodular program, we ob- tain fast scalable selection algorithms with mathematical performance guarantees, re- sulting in a unified framework that clarifies existing approaches and also makes …...

Automatic metrics are widely used in ma- chine translation as a substitute for hu- man assessment. With the...

Punctuation prediction and disfluency pre- diction can improve downstream natural language processing tasks such as ma- chine translation and information extrac- tion. Combining the two tasks can poten- tially improve the efficiency of the over- all pipeline system and reduce error prop- agation. In this work1, we compare var- ious …...