Articles

This paper addresses the problem of mor- phological modeling in statistical speech-to- speech translation for English to Iraqi Ara- bic. An analysis of user data from a real-time MT-based dialog system showed that generat- ing correct verbal inflections is a key problem for this language pair. We approach this prob- …...

This paper gives a detailed experiment feed- back of different approaches to adapt a sta- tistical machine translation system towards a targeted translation project, using only small amounts of parallel in-domain data. The experiments were performed by professional translators under realistic conditions of work using a computer assisted translation tool. …...

This paper proposes a novel approach to train word embeddings to capture antonyms. Word embeddings have shown to capture synonyms and analogies. Such word embeddings, how- ever, cannot capture antonyms since they de- pend on the distributional hypothesis. Our approach utilizes supervised synonym and antonym information from thesauri, as well …...

Vectorial representations of words derived from large current events datasets have been shown to perform well on word similarity tasks. This paper shows vectorial representa- tions derived from substantially smaller ex- planatory text datasets such as English Wikipedia and Simple English Wikipedia pre- serve enough lexical semantic information to make …...

This paper presents the first attempt to use word embeddings to predict the composition- ality of multiword expressions. We consider both single- and multi-prototype word em- beddings. Experimental results show that, in combination with a back-off method based on string similarity, word embeddings out- perform a method using count-based distribu- …...

Distributional representations of words have been recently used in supervised settings for recognizing lexical inference relations be- tween word pairs, such as hypernymy and en- tailment. We investigate a collection of these state-of-the-art methods, and show that they do not actually learn a relation between two words. Instead, they learn …...

We introduce the challenge of detecting se- mantically compatible words, that is, words that can potentially refer to the same thing (cat and hindrance are compatible, cat and dog are not), arguing for its central role in many se- mantic tasks. We present a publicly available data-set of human compatibility …...

Machine transliteration is often referred to as phonetic translation. We show that transliterations incorporate information from both spelling and pronunciation, and pro- pose an effective model for joint transliter- ation generation from both representations. We further generalize this model to include transliterations from other languages, and en- hance it with …...

We approach the task of morphological inflec- tion generation as discriminative string trans- duction. Our supervised system learns to gen- erate word-forms from lemmas accompanied by morphological tags, and refines them by re- ferring to the other forms within a paradigm. Results of experiments on six diverse lan- guages with …...

Methods of deep neural networks (DNNs) have recently demonstrated superior perfor- mance on a number of natural language pro- cessing tasks. However, in most previous work, the models are learned based on ei- ther unsupervised objectives, which does not directly optimize the desired task, or single- task supervised objectives, which …...

It is well known that prosodic information is used by infants in early language acquisition. In particular, prosodic boundaries have been shown to help infants with sentence and word- level segmentation. In this study, we extend an unsupervised method for word segmen- tation to include information about prosodic boundaries. The …...

We describe a novel max-margin learn- ing approach to optimize non-linear perfor- mance measures for distantly-supervised re- lation extraction models. Our approach can be generally used to learn latent variable models under multivariate non-linear perfor- mance measures, such as Fβ-score. Our approach interleaves Concave-Convex Pro- cedure (CCCP) for populating latent …...

We present a new deep learning architecture Bi-CNN-MI for paraphrase identification (PI). Based on the insight that PI requires compar- ing two sentences on multiple levels of granu- larity, we learn multigranular sentence repre- sentations using convolutional neural network (CNN) and model interaction features at each level. These features are …...

In modern practice, labeling a dataset of- ten involves aggregating annotator judgments obtained from crowdsourcing. State-of-the- art aggregation is performed via inference on probabilistic models, some of which are data- aware, meaning that they leverage features of the data (e.g., words in a document) in addi- tion to annotator judgments. …...

We present penalized expectation propaga- tion (PEP), a novel algorithm for approximate inference in graphical models. Expectation propagation is a variant of loopy belief prop- agation that keeps messages tractable by pro- jecting them back into a given family of func- tions. Our extension, PEP, uses a structured- sparsity penalty …...

Automatic analysis of impaired speech for screening or diagnosis is a growing research field; however there are still many barriers to a fully automated approach. When automatic speech recognition is used to obtain the speech transcripts, sentence boundaries must be in- serted before most measures of syntactic com- plexity can …...

We describe how a question-answering sys- tem can learn about its domain from conver- sational dialogs. Our system learns to relate concepts in science questions to propositions in a fact corpus, stores new concepts and re- lations in a knowledge graph (KG), and uses the graph to solve questions. We …...

This paper presents an in-depth investiga- tion on integrating neural language models in translation systems. Scaling neural lan- guage models is a difficult task, but crucial for real-world applications. This paper eval- uates the impact on end-to-end MT quality of both new and existing scaling techniques. We show when explicitly …...

The Bechdel test is a sequence of three questions designed to assess the presence of women in movies. Many believe that be- cause women are seldom represented in film as strong leaders and thinkers, viewers asso- ciate weaker stereotypes with women. In this paper, we present a computational approach to …...

Coreference resolution is a key problem in natural language understanding that still es- capes reliable solutions. One fundamental dif- ficulty has been that of resolving instances involving pronouns since they often require deep language understanding and use of back- ground knowledge. In this paper we pro- pose an algorithmic solution …...