Articles

One major drawback of phrase-based translation is that it segments an input sen- tence into continuous phrases. To sup- port linguistically informed source discon- tinuity, in this paper we construct graphs which combine bigram and dependency relations and propose a graph-based trans- lation model. The model segments an input graph …...

Neural Machine Translation (NMT) has obtained state-of-the art performance for several language pairs, while only us- ing parallel data for training. Target- side monolingual data plays an impor- tant role in boosting fluency for phrase- based statistical machine translation, and we investigate the use of monolingual data for NMT. In …...

We apply phrase-based and neural models to a core task in interactive machine trans- lation: suggesting how to complete a par- tial translation. For the phrase-based sys- tem, we demonstrate improvements in sug- gestion quality using novel objective func- tions, learning techniques, and inference algorithms tailored to this task. Our …...

When a large-scale incident or disaster oc- curs, there is often a great demand for rapidly developing a system to extract detailed and new information from low- resource languages (LLs). We propose a novel approach to discover compara- ble documents in high-resource languages (HLs), and project Entity Discovery and Linking …...

Slot filling aims to extract the values (slot fillers) of specific attributes (slots types) for a given entity (query) from a large- scale corpus. Slot filling remains very challenging over the past seven years. We propose a simple yet effective unsuper- vised approach to extract slot fillers based on the …...

Semantic parsing aims at mapping nat- ural language to machine interpretable meaning representations. Traditional ap- proaches rely on high-quality lexicons, manually-built templates, and linguis- tic features which are either domain- or representation-specific. In this pa- per we present a general method based on an attention-enhanced encoder-decoder model. We encode input …...

A core problem in learning semantic parsers from denotations is picking out consistent logical forms—those that yield the correct denotation—from a combina- torially large space. To control the search space, previous work relied on restricted set of rules, which limits expressivity. In this paper, we consider a much more ex- …...

Modeling crisp logical regularities is cru- cial in semantic parsing, making it difficult for neural models with no task-specific prior knowledge to achieve good results. In this paper, we introduce data recom- bination, a novel framework for inject- ing such prior knowledge into a model. From the training data, we …...

Attention mechanism has enhanced state- of-the-art Neural Machine Translation (NMT) by jointly learning to align and translate. It tends to ignore past alignment information, however, which often leads to over-translation and under-translation. To address this problem, we propose coverage-based NMT in this paper. We maintain a coverage vector to keep …...

Meaning Representation parsing James Goodman∗ Andreas Vlachos† Jason Naradowsky∗ ∗ Computer Science Department, University College London james@janigo.co.uk, jason.narad@gmail.com † Department of Computer Science, University of Sheffield a.vlachos@sheffield.ac.uk Abstract Semantic parsers map natural language statements into meaning representations, and must abstract over syntactic phenom- ena, resolve anaphora, and identify word senses …...

Precise evaluation metrics are important for assessing progress in high-level lan- guage generation tasks such as machine translation or image captioning. Histor- ically, these metrics have been evaluated using correlation with human judgment. However, human-derived scores are often alarmingly inconsistent and are also limited in their ability to identify precise …...

We cast sentence boundary detection and syntactic parsing as a joint problem, so an entire text document forms a training instance for transition-based dependency parsing. When trained with an early up- date or max-violation strategy for inexact search, we observe that only a tiny part of these very long training …...

Finding paraphrases in text is an impor- tant task with implications for genera- tion, summarization and question answer- ing, among other applications. Of par- ticular interest to those applications is the specific formulation of the task where the paraphrases are templated, which provides an easy way to lexicalize one message …...

Named Entity Disambiguation (NED) al- gorithms disambiguate mentions of named entities with respect to a knowledge-base, but sometimes the context might be poor or misleading. In this paper we introduce the acquisition of two kinds of background information to alleviate that problem: en- tity similarity and selectional preferences for syntactic …...

We present a computational analysis of three language varieties: native, advanced non-native, and translation. Our goal is to investigate the similarities and differ- ences between non-native language pro- ductions and translations, contrasting both with native language. Using a collec- tion of computational methods we estab- lish three main results: (1) …...

Foreign language learners can acquire new vocabulary by using cognate and con- text clues when reading. To measure such incidental comprehension, we devise an experimental framework that involves reading mixed-language “macaronic” sen- tences. Using data collected via Ama- zon Mechanical Turk, we train a graphi- cal model to simulate a …...

We present half-life regression (HLR), a novel model for spaced repetition practice with applications to second language ac- quisition. HLR combines psycholinguis- tic theory with modern machine learning techniques, indirectly estimating the “half- life” of a word or concept in a student’s long-term memory. We use data from Duolingo — …...

There has been almost no work on phrase structure annotation and parsing specially designed for learner English despite the fact that they are useful for representing the structural characteristics of learner En- glish. To address this problem, in this pa- per, we first propose a phrase structure an- notation scheme …...

This paper presents a problem-reduction approach to extractive multi-document summarization: we propose a reduction to the problem of scoring individual sen- tences with their ROUGE scores based on supervised learning. For the summariza- tion, we solve an optimization problem where the ROUGE score of the selected summary sentences is maximized. …...

Online news editors ask themselves the same question many times: what is miss- ing in this news article to go online? This is not an easy question to be answered by computational linguistic methods. In this work, we address this important question and characterise the constituents of news article editorial …...