Articles

We present a novel method for aligning a se- quence of instructions to a video of some- one carrying out a task. In particular, we fo- cus on the cooking domain, where the instruc- tions correspond to the recipe. Our technique relies on an HMM to align the recipe steps …...

Feature-Rich Space Carlos A. Colmenares∗ Google Inc. Brandschenkestrasse 110 8002 Zurich, Switzerland crcarlos@google.com Marina Litvak Shamoon College of Engineering Beer Sheva, Israel marinal@sce.ac.il Amin Mantrach Fabrizio Silvestri Yahoo Labs. Avinguda Diagonal 177 08018 Barcelona, Spain {amantrach,silvestr}@yahoo-inc.com Abstract Automatic headline generation is a sub-task of document summarization with many re- ported …...

We present a statistical framework to extract information-rich citation sentences that sum- marise the main contributions of a scientific paper. In a first stage, we automatically dis- cover salient keywords from a paper’s citation summary, keywords that characterise its main contributions. In a second stage, exploiting the results of the …...

Syntactic linearization algorithms take a bag of input words and a set of optional con- straints, and construct an output sentence and its syntactic derivation simultaneously. The search problem is NP-hard, and the current best results are achieved by bottom-up best- first search. One drawback of the method is low …...

Convolutional neural network (CNN) is a neu- ral network that can make use of the inter- nal structure of data such as the 2D structure of image data. This paper studies CNN on text categorization to exploit the 1D structure (namely, word order) of text data for accurate prediction. Instead …...

Character n-grams have been identified as the most successful feature in both single- domain and cross-domain Authorship Attribu- tion (AA), but the reasons for their discrimina- tive value were not fully understood. We iden- tify subgroups of character n-grams that corre- spond to linguistic aspects commonly claimed to be covered …...

In this paper, we apply a weakly-supervised learning approach for slot tagging using con- ditional random fields by exploiting web search click logs. We extend the constrained lattice training of T¨ackstr¨om et al. (2013) to non-linear conditional random fields in which latent variables mediate between observations and labels. When combined …...

Text documents of varying nature (e.g., sum- mary documents written by analysts or pub- lished, scientific papers) often cite others as a means of providing evidence to support a claim, attributing credit, or referring the reader to related work. We address the problem of predicting a document’s cited sources by …...

Incremental parsers have potential advantages for applications like language modeling for machine translation and speech recognition. We describe a new algorithm for incremental transition-based Combinatory Categorial Grammar parsing. As English CCGbank derivations are mostly right branching and non-incremental, we design our algorithm based on the dependencies resolved rather than the …...

Parsing full-fledged predicate-argument struc- tures in a deep syntax framework requires graphs to be predicted. Using the DeepBank (Flickinger et al., 2012) and the Predicate- Argument Structure treebank (Miyao and Tsu- jii, 2005) as a test field, we show how transition-based parsers, extended to handle connected graphs, benefit from the …...

In this paper, we introduce a new approach for joint segmentation, POS tagging and de- pendency parsing. While joint modeling of these tasks addresses the issue of error prop- agation inherent in traditional pipeline archi- tectures, it also complicates the inference task. Past research has addressed this challenge by placing …...

Vector Space Models (VSMs) of Semantics are useful tools for exploring the semantics of single words, and the composition of words to make phrasal meaning. While many meth- ods can estimate the meaning (i.e. vector) of a phrase, few do so in an interpretable way. We introduce a new method …...

Most recent unsupervised methods in vector space semantics for assessing thematic fit (e.g. Erk, 2007; Baroni and Lenci, 2010; Sayeed and Demberg, 2014) create prototypical role- fillers without performing word sense disam- biguation. This leads to a kind of sparsity problem: candidate role-fillers for different senses of the verb end …...

We present a joint model for predicate argu- ment alignment. We leverage multiple sources of semantic information, including temporal ordering constraints between events. These are combined in a max-margin framework to find a globally consistent view of entities and events across multiple documents, which leads to improvements over a very …...

We introduce a new approach to unsupervised estimation of feature-rich semantic role la- beling models. Our model consists of two components: (1) an encoding component: a semantic role labeling model which predicts roles given a rich set of syntactic and lexi- cal features; (2) a reconstruction component: a tensor factorization …...

In this paper, we enhance the traditional confusion network system combination approach with an additional model trained by a neural network. This work is moti- vated by the fact that the commonly used binary system voting models only assign each input system a global weight which is responsible for the …...

In this paper, we take a closer look at the MT evaluation process from a glass-box perspective using eye-tracking. We an- alyze two aspects of the evaluation task – the background of evaluators (monolin- gual or bilingual) and the sources of in- formation available, and we evaluate them using time …...

In this paper, we describe our submis- sion to WMT 2015 Tuning Task. We integrate a dependency-based MT evalu- ation metric, RED, to Moses and com- pare it with BLEU and METEOR in con- junction with two tuning methods: MERT and MIRA. Experiments are conducted us- ing hierarchical phrase-based models …...

We define a new algorithm, named “Drem”, for tuning the weighted linear model in a statistical machine translation system. Drem has two major innova- tions. First, it uses scaled derivative-free trust-region optimization rather than other methods’ line search or (sub)gradient ap- proximations. Second, it interpolates the decoder output, using information …...

Human-designed sub-structures are re- quired by most of the syntax-based ma- chine translation evaluation metrics. In this paper, we propose a novel evalua- tion metric based on dependency parsing model, which does not need this human in- volvement. Experimental results show that the new single metric gets better correla- tion …...