Articles

We introduce Trans-gram, a simple and computationally-efficient method to simultaneously learn and align word- embeddings for a variety of languages, us- ing only monolingual data and a smaller set of sentence-aligned data. We use our new method to compute aligned word- embeddings for twenty-one languages us- ing English as a …...

Language use is known to be influenced by personality traits as well as by socio- demographic characteristics such as age or mother tongue. As a result, it is possible to au- tomatically identify these traits of the author from her texts. It has recently been shown that knowledge of such …...

In syntax-based machine translation, rule selection is the task of choosing the cor- rect target side of a translation rule among rules with the same source side. We de- fine a discriminative rule selection model for systems that have syntactic annota- tion on the target language side (string- to-tree). This …...

This paper proposes a method for hierar- chical phrase-based stream decoding. A stream decoder is able to take a contin- uous stream of tokens as input, and seg- ments this stream into word sequences that are translated and output as a stream of tar- get word sequences. Phrase-based stream decoding …...

This work focuses on the task of finding latent vector representations of the words in a corpus. In particular, we address the issue of what to do when there are multiple languages in the corpus. Prior work has, among other techniques, used canonical correlation analysis to project pre-trained vectors in …...

In this paper, we develop a supervised learning technique that improves noisy phrase translation scores obtained by phrase table triangulation. In particular, we extract word translation distributions from small amounts of source-target bilin- gual data (a dictionary or a parallel corpus) with which we learn to assign better scores to …...

We present novel features designed with a deep neural network for Machine Trans- lation (MT) Quality Estimation (QE). The features are learned with a Continuous Space Language Model to estimate the probabilities of the source and target seg- ments. These new features, along with standard MT system-independent features, are benchmarked …...

Many state-of-the-art Machine Translation (MT) evaluation metrics are complex, in- volve extensive external resources (e.g. for paraphrasing) and require tuning to achieve best results. We present a simple alternative approach based on dense vec- tor spaces and recurrent neural networks (RNNs), in particular Long Short Term Memory (LSTM) networks. For …...

Discourse relations can be categorized as continuous or discontinuous in the hypoth- esis of continuity (Murray, 1997), with continuous relations expressing normal succession of events in discourse such as temporal, spatial or causal. Asr and Dem- berg (2013) propose a markedness mea- sure to test the prediction that discontin- uous …...

We present an incremental adaptation ap- proach for statistical machine translation that maintains a flexible hierarchical do- main structure within a single consistent model. Both weights and rules are updated incrementally on a stream of post-edits. Our multi-level domain hierarchy allows the sys- tem to adapt simultaneously towards local context …...

Continuous-space translation models have recently emerged as extremely powerful ways to boost the performance of existing translation systems. A simple, yet effec- tive way to integrate such models in infer- ence is to use them in an N-best rescor- ing step. In this paper, we focus on this scenario and …...

Logic grid puzzle is a genre of logic puz- zles in which we are given (in a natural language) a scenario, the object to be de- duced and certain clues. The reader has to figure out the solution using the clues provided and some generic domain con- straints. In this …...

Taxonomy plays an important role in many applications by organizing domain knowl- edge into a hierarchy of is-a relations be- tween terms. Previous works on the taxo- nomic relation identification from text cor- pora lack in two aspects: 1) They do not consider the trustiness of individual source texts, which …...

In this paper, we propose a paraphrasing model to address the task of system com- bination for machine translation. We dy- namically learn hierarchical paraphrases from target hypotheses and form a syn- chronous context-free grammar to guide a series of transformations of target hy- potheses into fused translations. The model …...

Sarcasm is generally characterized as a figure of speech that involves the substi- tution of a literal by a figurative mean- ing, which is usually the opposite of the original literal meaning. We re-frame the sarcasm detection task as a type of word sense disambiguation problem, where the sense of …...

We present an unsupervised hard EM ap- proach to automatically mapping instruc- tional recipes to action graphs, which de- fine what actions should be performed on which objects and in what order. Recov- ering such structures can be challenging, due to unique properties of procedural lan- guage where, for example, …...

Relational phrases (e.g., “got married to”) and their hypernyms (e.g., “is a relative of”) are central for many tasks including question answering, open information ex- traction, paraphrasing, and entailment de- tection. This has motivated the develop- ment of several linguistic resources (e.g. DIRT, PATTY, and WiseNet) which sys- tematically collect …...

We present a new method for semantic role labeling in which arguments and seman- tic roles are jointly embedded in a shared vector space for a given predicate. These embeddings belong to a neural network, whose output represents the potential func- tions of a graphical model designed for the SRL …...

We introduce pre-post-editing, possibly the most basic form of interactive trans- lation, as a touch-based interaction with iteratively improved translation hypothe- ses prior to classical post-editing. We re- port simulated experiments that yield very large improvements on classical evalua- tion metrics (up to 21 BLEU) as well as on a …...

Monolingual alignment is the task of pair- ing semantically similar units from two pieces of text. We report a top-performing supervised aligner that operates on short text snippets. We employ a large feature set to (1) encode similarities among semantic units (words and named entities) in context, and (2) address …...