Articles

Many NLP systems use dependency parsers as critical components. Jonit learn- ing parsers usually achieve better parsing accuracies than two-stage methods. How- ever, classical joint parsing algorithms significantly increase computational com- plexity, which makes joint learning im- practical. In this paper, we proposed an ef- ficient dependency parsing algorithm that …...

Parameter tuning is a key problem for sta- tistical machine translation (SMT). Most popular parameter tuning algorithms for SMT are agnostic of decoding, result- ing in parameters vulnerable to search er- rors in decoding. The recent research of “search-aware tuning” (Liu and Huang, 2014) addresses this problem by consid- ering …...

The information conveyed by some sen- tences would be more easily understood by a reader if it were expressed in multi- ple sentences. We call such sentences con- tent heavy: these are possibly grammatical but difficult to comprehend, cumbersome sentences. In this paper we introduce the task of detecting content-heavy …...

We describe an approach for machine learning-based empty category detection that is based on the phrase structure analy- sis of Japanese. The problem is formal- ized as tree node classification, and we find that the path feature, the sequence of node labels from the current node to the root, is …...

We present novel models for domain adap- tation based on the neural network joint model (NNJM). Our models maximize the cross entropy by regularizing the loss function with respect to in-domain model. Domain adaptation is carried out by as- signing higher weight to out-domain se- quences that are similar to …...

Learning semantic representations and tree structures of bilingual phrases is ben- eficial for statistical machine translation. In this paper, we propose a new neu- ral network model called Bilingual Corre- spondence Recursive Autoencoder (BCor- rRAE) to model bilingual phrases in trans- lation. We incorporate word alignments into BCorrRAE to allow …...

Lexical selection is of great importance to statistical machine translation. In this paper, we propose a graph-based frame- work for collective lexical selection. The framework is established on a translation graph that captures not only local associ- ations between source-side content words and their target translations but also target- side …...

We investigate a combination of a tra- ditional linear sparse feature model and a multi-layer neural network model for deterministic transition-based dependency parsing, by integrating the sparse features into the neural model. Correlations are drawn between the hybrid model and pre- vious work on integrating word embed- ding features into …...

As conventional word alignment search algorithms usually ignore the consistency constraint in translation rule extraction, improving alignment accuracy does not necessarily increase translation quality. We propose to use coverage, which reflects how well extracted phrases can recover the training data, to enable word alignment to model consistency and corre- late …...

In hierarchical phrase-based machine translation, a rule table is automatically learned by heuristically extracting syn- chronous rules from a parallel corpus. As a result, spuriously many rules are extracted which may be composed of various incorrect rules. The larger rule table incurs more run time for decoding and may result …...

We investigate the need for bigram align- ment models and the benefit of super- vised alignment techniques in grapheme- to-phoneme (G2P) conversion. Moreover, we quantitatively estimate the relation- ship between alignment quality and over- all G2P system performance. We find that, in English, bigram alignment models do perform better than …...

Currently most of state-of-the-art meth- ods for Chinese word segmentation are based on supervised learning, whose fea- tures are mostly extracted from a local con- text. These methods cannot utilize the long distance information which is also crucial for word segmentation. In this paper, we propose a novel neural network …...

This paper describes an alignment-based model for interpreting natural language in- structions in context. We approach in- struction following as a search over plans, scoring sequences of actions conditioned on structured observations of text and the environment. By explicitly modeling both the low-level compositional structure of individual actions and the …...

In this paper we propose a framework to improve word segmentation accuracy us- ing input method logs. An input method is software used to type sentences in lan- guages which have far more characters than the number of keys on a keyboard. The main contributions of this paper are: 1) …...

This paper presents a bilingual semi- supervised Chinese word segmentation (CWS) method that leverages the nat- ural segmenting information of English sentences. The proposed method in- volves learning three levels of features, namely, character-level, phrase-level and sentence-level, provided by multiple sub- models. We use a sub-model of condi- tional random …...

According to the principle of composi- tionality, the meaning of a sentence is computed from the meaning of its parts and the way they are syntactically com- bined. In practice, however, the syntactic structure is computed by automatic parsers which are far-from-perfect and not tuned to the specifics of the …...

Meaning Representation Using Syntax-Based Machine Translation Michael Pust, Ulf Hermjakob, Kevin Knight, Daniel Marcu, Jonathan May Information Sciences Institute Computer Science Department University of Southern California {pust, ulf, knight, marcu, jonmay}@isi.edu Abstract We present a parser for Abstract Meaning Representation (AMR). We treat English- to-AMR conversion within the framework of …...

This paper presents a semantic parsing and reasoning approach to automatically solving math word problems. A new meaning representation language is de- signed to bridge natural language text and math expressions. A CFG parser is imple- mented based on 9,600 semi-automati- cally created grammar rules. We conduct experiments on a …...

The phonotactics of a language describes the ways in which the sounds of the language combine to form possible mor- phemes and words. Humans can learn phonotactic patterns at the level of abstract classes, generalizing across sounds (e.g., “words can end in a voiced stop”). More- over, they rapidly acquire …...

The widespread use of deception in online sources has motivated the need for meth- ods to automatically profile and identify deceivers. This work explores deception, gender and age detection in short texts us- ing a machine learning approach. First, we collect a new open domain deception dataset also containing demographic …...