Articles

In this paper, we propose a novel recursive recurrent neural network (R2NN) to mod- el the end-to-end decoding process for s- tatistical machine translation. R2NN is a combination of recursive neural network and recurrent neural network, and in turn integrates their respective capabilities: (1) new information can be used to …...

Bidirectional models of word alignment are an appealing alternative to post-hoc combinations of directional word align- ers. Unfortunately, most bidirectional formulations are NP-Hard to solve, and a previous attempt to use a relaxation- based decoder yielded few exact solu- tions (6%). We present a novel relax- ation for decoding the …...

This study proposes a word alignment model based on a recurrent neural net- work (RNN), in which an unlimited alignment history is represented by re- currently connected hidden layers. We perform unsupervised learning using noise-contrastive estimation (Gutmann and Hyv¨arinen, 2010; Mnih and Teh, 2012), which utilizes artificially generated negative samples. …...

The sense in which a word is used deter- mines the translation of the word. In this paper, we propose a sense-based transla- tion model to integrate word senses into statistical machine translation. We build a broad-coverage sense tagger based on a nonparametric Bayesian topic model that automatically learns sense …...

We present a novel technique for semantic frame identification using distributed rep- resentations of predicates and their syntac- tic context; this technique leverages auto- matic syntactic parses and a generic set of word embeddings. Given labeled data annotated with frame-semantic parses, we learn a model that projects the set of …...

We present an approach for learning context-dependent semantic parsers to identify and interpret time expressions. We use a Combinatory Categorial Gram- mar to construct compositional meaning representations, while considering contex- tual cues, such as the document creation time and the tense of the governing verb, to compute the final time …...

Meaning Representation Jeffrey Flanigan Sam Thomson Jaime Carbonell Chris Dyer Noah A. Smith Language Technologies Institute Carnegie Mellon University Pittsburgh, PA 15213, USA {jflanigan,sthomson,jgc,cdyer,nasmith}@cs.cmu.edu Abstract Abstract Meaning Representation (AMR) is a semantic formalism for which a grow- ing set of annotated examples is avail- able. We introduce the first approach …...

A central challenge in semantic parsing is handling the myriad ways in which knowl- edge base predicates can be expressed. Traditionally, semantic parsers are trained primarily from text paired with knowledge base information. Our goal is to exploit the much larger amounts of raw text not tied to any knowledge …...

Following up on recent work on estab- lishing a mapping between vector-based semantic embeddings of words and the visual representations of the correspond- ing objects from natural images, we first present a simple approach to cross-modal vector-based semantics for the task of zero-shot learning, in which an image of a …...

We present CoSimRank, a graph-theoretic similarity measure that is efficient because it can compute a single node similarity without having to compute the similarities of the entire graph. We present equivalent formalizations that show CoSimRank’s close relationship to Personalized Page- Rank and SimRank and also show how we can take …...

Accurate scoring of syntactic structures such as head-modifier arcs in dependency parsing typically requires rich, dimensional feature representations.high- small subset of such features is often se-A lected manually. This is problematic when features lack clear linguistic meaning as in embeddings or when the information is blended across features. In this …...

Recent work has shown success in us- ing neural network language models (NNLMs) as features in MT systems. Here, we present a novel formulation for a neural network joint model (NNJM), which augments the NNLM with a source context window. Our model is purely lexi- calized and can be integrated …...

This study investigates on building a better Chinese word segmentation mod- el for statistical machine translation. aims at leveraging word boundary infor-It mation, automatically learned by bilin- gual character-based alignments, to induce a preferable segmentation model. We propose dealing with the induced word boundaries as soft constraints to bias the …...

We present a novel way of generating un- seen words, which is useful for certain ap- plications such as automatic speech recog- nition or optical character recognition in low-resource languages. We test our vo- cabulary generator on seven low-resource languages by measuring the decrease in out-of-vocabulary word rate on a …...

We present a novel approach for induc- ing unsupervised dependency parsers for languages that have no labeled training data, but have translated text in a resource- rich language. We train probabilistic pars- ing models for resource-poor languages by transferring cross-lingual knowledge from resource-rich language with entropy reg- ularization. Our method …...

We aim to improve spoken term detec- tion performance by incorporating con- textual information beyond traditional N- gram language models. Instead of taking a broad view of topic context in spoken doc- uments, variability of word co-occurrence statistics across corpora leads us to fo- cus instead the on phenomenon of …...

Designing measures that capture various aspects of language ability is a central task in the design of systems for auto- matic scoring of spontaneous speech. In this study, we address a key aspect of lan- guage proficiency assessment – syntactic complexity. We propose a novel measure of syntactic complexity for …...

Large-scale knowledge bases are impor- tant assets in NLP. Frequently, such re- sources are constructed through automatic mergers of complementary resources, such as WordNet and Wikipedia. However, manually validating these resources is pro- hibitively expensive, even when using methods such as crowdsourcing. We pro- pose a cost-effective method of validat- …...

Recent work on Chinese analysis has led to large-scale annotations of the internal structures of words, enabling character- level analysis of Chinese syntactic struc- tures. In this paper, we investigate the problem of character-level Chinese depen- dency parsing, building dependency trees over characters. Character-level infor- mation can benefit downstream applica- …...

The main work in bilingual lexicon ex- traction from comparable corpora is based on the implicit hypothesis that corpora are balanced. However, the historical context- based projection method dedicated to this task is relatively insensitive to the sizes of each part of the comparable corpus. Within this context, we have …...