Articles

Recent work has shown success in us- ing continuous word embeddings learned from unlabeled data as features to improve supervised NLP systems, which is re- garded as a simple semi-supervised learn- ing mechanism. However, fundamen- tal problems on effectively incorporating the word embedding features within the framework of linear models …...

Distinct properties of translated text have been the subject of research in linguistics for many year (Baker, 1993). In recent years computational methods have been developed to empirically verify the lin- guistic theories about translated text (Ba- roni and Bernardini, 2006). While many characteristics of translated text are more apparent …...

Microblogs have recently received widespread interest from NLP re- searchers. However, current tools for Japanese word segmentation and POS tagging still perform poorly on microblog texts. We developed an annotated corpus and proposed a joint model for over- coming this situation. Our annotated corpus of microblog texts enables not only …...

We present STIR (STrongly Incremen- tal Repair detection), a system that de- tects speech repairs and edit terms on transcripts incrementally with minimal la- tency. STIR uses information-theoretic measures from n-gram models as its prin- cipal decision features in a pipeline of classifiers detecting the different stages of repairs. Results …...

Discourse parsing is a challenging task and plays a critical role in discourse anal- ysis. In this paper, we focus on label- ing full argument spans of discourse con- nectives in the Penn Discourse Treebank (PDTB). Previous studies cast this task as a linear tagging or subtree extraction problem. In …...

This paper proposes a Markov Decision Process and reinforcement learning based approach for domain selection in a multi- domain Spoken Dialogue System built on a distributed architecture. In the proposed framework, the domain selection prob- lem is treated as sequential planning in- stead of classification, such that confir- mation and …...

In this paper, we present a novel ap- proach for identifying argumentative dis- course structures in persuasive essays. The structure of argumentation consists of sev- eral components (i.e. claims and premises) that are connected with argumentative re- lations. We consider this task in two consecutive steps. First, we identify the …...

We construct multi-modal concept repre- sentations by concatenating a skip-gram linguistic representation vector with a vi- sual concept representation vector com- puted using the feature extraction layers of a deep convolutional neural network (CNN) trained on a large labeled object recognition dataset. This transfer learn- ing approach brings a clear …...

This work presents two different trans- lation models using recurrent neural net- works. The first one is a word-based ap- proach using word alignments. Second, we present phrase-based translation mod- els that are more consistent with phrase- based decoding. Moreover, we introduce bidirectional recurrent neural models to the problem of …...

This paper presents a deep semantic simi- larity model (DSSM), a special type of deep neural networks designed for text analysis, for recommending target docu- ments to be of interest to a user based on a source document that she is reading. We observe, identify, and detect naturally oc- curring …...

There is rich knowledge encoded in on- line web data. For example, punctua- tion and entity tags in Wikipedia data define some word boundaries in a sen- tence. In this paper we adopt partial-label learning with conditional random fields to make use of this valuable knowledge for semi-supervised Chinese word …...

This paper investigates the use of neural networks for the acquisition of selectional preferences. Inspired by recent advances of neural network models for NLP applica- tions, we propose a neural network model that learns to discriminate between felici- tous and infelicitous arguments for a par- ticular predicate. The model is …...

A typical discussion thread in an online fo- rum spans multiple pages involving par- ticipation from multiple users and thus, may contain multiple view-points and so- lutions. A user interested in the topic of discussion or having a problem similar to being discussed in the thread may not want to …...

We propose a novel search-based approach for greedy coreference resolution, where the mentions are processed in order and added to previous coreference clusters. Our method is distinguished by the use of two functions to make each corefer- ence decision: a pruning function that prunes bad coreference decisions from fur- ther …...

In this paper, we propose a Connective- driven Dependency Tree (CDT) scheme to represent the discourse rhetorical structure in Chinese language, with ele- mentary discourse units as leaf nodes and connectives as non-leaf nodes, large- ly motivated by the Penn Discourse Treebank and the Rhetorical Structure Theory. In particular, connectives …...

Unlike traditional over-the-phone spoken dialog systems (SDSs), modern dialog systems tend to have visual rendering on the device screen as an additional modal- ity to communicate the system’s response to the user. Visual display of the system’s response not only changes human behav- ior when interacting with devices, but also …...

Coherence is what makes a multi-sentence text meaningful, both logically and syn- tactically. To solve the challenge of or- dering a set of sentences into coherent or- der, existing approaches focus mostly on defining and using sophisticated features to capture the cross-sentence argumenta- tion logic and syntactic relationships. But both …...

In this paper, we present a discrimina- tive approach for reranking discourse trees generated by an existing probabilistic dis- course parser. The reranker relies on tree kernels (TKs) to capture the global depen- dencies between discourse units in a tree. In particular, we design new computa- tional structures of discourse …...

We present a novel method for coreference resolution error analysis which we apply to perform a recall error analysis of four state-of-the-art English coreference reso- lution systems. Our analysis highlights differences between the systems and iden- tifies that the majority of recall errors for nouns and names are shared by …...

Text-level discourse parsing remains a challenge: most approaches employ fea- tures that fail to capture the intentional, se- mantic, and syntactic aspects that govern discourse coherence. In this paper, we pro- pose a recursive model for discourse pars- ing that jointly models distributed repre- sentations for clauses, sentences, and en- …...