Articles

We present a dual decomposition frame- work for multi-document summarization, using a model that jointly extracts and compresses sentences. Compared with previous work based on integer linear pro- gramming, our approach does not require external solvers, is significantly faster, and is modular in the three qualities a sum- mary should …...

Supervised topic models with a logistic likelihood have two issues that potential- ly limit their practical use: 1) response variables are usually over-weighted by document word counts; and 2) existing variational inference methods make strict mean-field assumptions. We address these issues by: 1) introducing a regularization constant to better balance …...

In the ever-expanding sea of microblog data, there is a surprising amount of naturally occurring par- allel text: some users create post multilingual mes- sages targeting international audiences while oth- ers “retweet” translations. We present an efficient method for detecting these messages and extract- ing parallel segments from them. We …...

In this paper, we explore a novel bilin- gual word alignment approach based on DNN (Deep Neural Network), which has been proven to be very effective in var- ious machine learning tasks (Collobert et al., 2011). We describe in detail how we adapt and extend the CD-DNN- HMM (Dahl et …...

This paper proposes new distortion mod- els for phrase-based SMT. In decoding, a distortion model estimates the source word position to be translated next (NP) given the last translated source word position (CP). We propose a distortion model that can consider the word at the CP, a word at an …...

Binarization of grammars is crucial for im- proving the complexity and performance of parsing and translation. We present a versatile binarization algorithm that can be tailored to a number of grammar for- malisms by simply varying a formal pa- rameter. We apply our algorithm to bi- narizing tree-to-string transducers used …...

We present a novel transition-based, greedy dependency parser which implements a flexible mix of bottom-up and top-down strategies. The new strategy allows the parser to postpone difficult decisions until the relevant information becomes available. The novel parser has a ∼12% error reduc- tion in unlabeled attachment score over an arc-eager …...

Characters play an important role in the Chinese language, yet computational pro- cessing of Chinese has been dominated by word-based approaches, with leaves in syntax trees being words. We investigate Chinese parsing from the character-level, extending the notion of phrase-structure trees by annotating internal structures of words. We demonstrate the …...

Efficiently incorporating entity-level in- formation is a challenge for coreference resolution systems due to the difficulty of exact inference over partitions. We de- scribe an end-to-end discriminative prob- abilistic model for coreference that, along with standard pairwise features, enforces structural agreement constraints between specified properties of coreferent men- tions. This …...

We propose a computationally efficient graph-based approach for local coherence modeling. We evaluate our system on three tasks: sentence ordering, summary coherence rating and readability assess- ment. The performance is comparable to entity grid based approaches though these rely on a computationally expensive train- ing phase and face data sparsity …...

Temporal resolution systems are tradition- ally tuned to a particular language, re- quiring significant human effort to trans- late them to new languages. We present a language independent semantic parser for learning the interpretation of tempo- ral phrases given only a corpus of utter- ances and the times they reference. …...

Traditional approaches to the task of ACE event extraction usually rely on sequential pipelines with multiple stages, which suf- fer from error propagation since event trig- gers and arguments are predicted in isola- tion by independent local classifiers. By contrast, we propose a joint framework based on structured prediction which …...

Recent work on statistical quantifier scope disambiguation (QSD) has improved upon earlier work by scoping an arbitrary num- ber and type of noun phrases. No corpus- based method, however, has yet addressed QSD when incorporating the implicit uni- versal of plurals and/or operators such as negation. In this paper we …...

We present a method that learns repre- sentations for word meanings from short video clips paired with sentences. Un- like prior work on learning language from symbolic input, our input consists of video of people interacting with multiple com- plex objects in outdoor environments. Un- like prior computer-vision approaches that …...

We present an algorithm for re-estimating parameters of backoff n-gram language models so as to preserve given marginal distributions, along the lines of well- known Kneser-Ney (1995) smoothing. Unlike Kneser-Ney, our approach is de- signed to be applied to any given smoothed backoff model, including models that have already been …...

Automated annotation of social behavior in conversation is necessary for large-scale analysis of real-world conversational data. Important behavioral categories, though, are often sparse and often appear only in specific subsections of a conversation. This makes supervised machine learning difficult, through a combination of noisy features and unbalanced class distribu- tions. …...

We derive variants of the fertility based models IBM-3 and IBM-4 that, while maintaining their zero and first order pa- rameters, are nondeficient. Subsequently, we proceed to derive a method to com- pute a likely alignment and its neighbors as well as give a solution of EM training. The arising …...

Since statistical machine translation (SMT) and translation memory (TM) complement each other in matched and unmatched regions, integrated models are proposed in this paper to incorporate TM information into phrase-based SMT. Unlike previous multi-stage pipeline approaches, which directly merge TM result into the final output, the proposed models refer to …...

We introduce a shift-reduce parsing algorithm for phrase-based string-to- dependency translation. As the algorithm generates dependency trees for partial translations left-to-right in decoding, it allows for efficient integration of both n-gram and dependency language mod- els. To resolve conflicts in shift-reduce parsing, we propose a maximum entropy model trained on …...

Annotating linguistic data is often a com- plex, time consuming and expensive en- deavour. Even with strict annotation guidelines, human subjects often deviate in their analyses, each bringing different biases, interpretations of the task and lev- els of consistency. We present novel tech- niques for learning from the outputs of …...