Articles

Treebanks are not large enough to adequately model subcategorization frames of predica- tive lexemes, which is an important source of lexico-syntactic constraints for parsing. As a consequence, parsers trained on such tree- banks usually make mistakes when selecting the arguments of predicative lexemes. In this paper, we propose an original …...

The study presented in this work is a first ef- fort at real-time speech translation of TED talks, a compendium of public talks with dif- ferent speakers addressing a variety of top- ics. We address the goal of achieving a sys- tem that balances translation accuracy and la- tency. In …...

Addressee detection (AD) is an important problem for dialog systems in human-human- computer scenarios (contexts involving mul- tiple people and a system) because system- directed speech must be distinguished from human-directed speech. Recent work on AD (Shriberg et al., 2012) showed good results using prosodic and lexical features trained on …...

Automatically assessing the fidelity of a retelling to the original narrative – a task of growing clinical importance – is challenging, given extensive paraphrasing during retelling along with cascading automatic speech recog- nition (ASR) errors. We present a word tag- ging approach using conditional random fields (CRFs) that allows a …...

We present a new variant of the Syntax- Augmented Machine Translation (SAMT) for- malism with a category-coarsening algorithm originally developed for tree-to-tree gram- mars. We induce bilingual labels into the SAMT grammar, use them for category coars- ening, then project back to monolingual la- beling as in standard SAMT. The …...

We present a framework for cross-lingual transfer of sequence information from a resource-rich source language to a resource- impoverished target language that incorporates soft constraints via posterior regularization. To this end, we use automatically word aligned bitext between the source and target language pair, and learn a discriminative conditional ran- …...

Many statistical learning problems in NLP call for local model search methods. But accu- racy tends to suffer with current techniques, which often explore either too narrowly or too broadly: hill-climbers can get stuck in local optima, whereas samplers may be inefficient. We propose to arrange individual local opti- mizers …...

Classical coreference systems encode various syntactic, discourse, and semantic phenomena explicitly, using heterogenous features com- puted from hand-crafted heuristics. In con- trast, we present a state-of-the-art coreference system that captures such phenomena implic- itly, with a small number of homogeneous feature templates examining shallow proper- ties of mentions. Surprisingly, our …...

Recent work has developed supervised meth- ods for detecting deceptive opinion spam— fake reviews written to sound authentic and deliberately mislead readers. And whereas past work has focused on identifying individ- ual fake reviews, this paper aims to identify offerings (e.g., hotels) that contain fake re- views. We introduce a …...

Minimum Error Rate Training (MERT) re- mains one of the preferred methods for tun- ing linear parameters in machine translation systems, yet it faces significant issues. First, MERT is an unregularized learner and is there- fore prone to overfitting. Second, it is com- monly used on a noisy, non-convex loss …...

Recommendation systems (RS) take advan- tage of products and users information in order to propose items to consumers. Collaborative, content-based and a few hybrid RS have been developed in the past. In contrast, we propose a new domain-independent semantic RS. By providing textually well-argued recommenda- tions, we aim to give …...

The problem to replace a word with a syn- onym that fits well in its sentential context is known as the lexical substitution task. In this paper, we tackle this task as a supervised ranking problem. Given a dataset of target words, their sentential contexts and the poten- tial substitutions …...

Traditional distributional semantic models ex- tract word meaning representations from co- occurrence patterns of words in text cor- pora. Recently, the distributional approach has been extended to models that record the co- occurrence of words with visual features in image collections. These image-based models should be complementary to text-based ones, …...

We present a new approach to referring ex- pression generation, casting it as a density es- timation problem where the goal is to learn distributions over logical expressions identi- fying sets of objects in the world. Despite an extremely large space of possible expres- sions, we demonstrate effective learning of …...

Constituency parsing with rich grammars re- mains a computational challenge. Graph- ics Processing Units (GPUs) have previously been used to accelerate CKY chart evalua- tion, but gains over CPU parsers were mod- est. In this paper, we describe a collection of new techniques that enable chart evaluation at close to …...

This short paper presents a pilot study in- vestigating the training of a standard Seman- tic Role Labeling (SRL) system on product reviews for the new task of detecting com- parisons. An (opinionated) comparison con- sists of a comparative “predicate” and up to three “arguments”: the entity evaluated posi- tively, …...

With the rapid growth of social media, Twitter has become one of the most widely adopted platforms for people to post short and instant message. On the one hand, people tweets about their daily lives, and on the other hand, when major events happen, people also fol- low and tweet …...

Work on authorship attribution has tradition- ally focused on long texts. In this work, we tackle the question of whether the author of a very short text can be successfully iden- tified. We use Twitter as an experimental testbed. We introduce the concept of an au- thor’s unique “signature”, and …...

This paper presents an approach for detecting promotional content in Wikipedia. By incor- porating stylometric features, including fea- tures based on n-gram and PCFG language models, we demonstrate improved accuracy at identifying promotional articles, compared to using only lexical information and meta- features. 1...

In this work, we argue that measures that have been shown to quantify the degree of semantic plausibility of phrases, as obtained from their compositionally-derived distributional seman- tic representations, can resolve syntactic am- biguities. We exploit this idea to choose the correct parsing of NPs (e.g., (live fish) trans- porter …...