Articles

This paper studies the use of structural representations for learning relations be- tween pairs of short texts (e.g., sentences or paragraphs) of the kind: the second text answers to, or conveys exactly the same information of, or is implied by, the first text. Engineering effective features that can capture syntactic …...

We focus on the task of interpreting com- plex natural language instructions to a robot, in which we must ground high-level commands such as microwave the cup to low-level actions such as grasping. Pre- vious approaches that learn a lexicon dur- ing training have inadequate coverage at test time, and …...

Meaning Representation Parsing Keenon Werling Stanford University keenon@stanford.edu Gabor Angeli Stanford University angeli@stanford.edu Christopher D. Manning Stanford University manning@stanford.edu Abstract The Abstract Meaning Representation (AMR) is a representation for open- domain rich semantics, with potential use in fields like event extraction and machine translation. Node generation, typically done using a …...

We introduce C-PHRASE, a distributional semantic model that learns word repre- sentations by optimizing context predic- tion for phrases at all levels in a syntactic tree, from single words to full sentences. C-PHRASE outperforms the state-of-the- art C-BOW model on a variety of lexical tasks. Moreover, since C-PHRASE word vectors …...

Existing methods for Japanese predicate argument structure (PAS) analysis identify case arguments of each predicate without considering interactions between the tar- get PAS and others in a sentence. How- ever, the argument structures of the pred- icates in a sentence are semantically re- lated to each other. This paper proposes …...

Selectional preferences (SPs) are widely used in NLP as a rich source of semantic information. While SPs have been tradi- tionally induced from textual data, human lexical acquisition is known to rely on both linguistic and perceptual experience. We present the first SP learning method that si- multaneously draws knowledge …...

Many high level natural language process- ing problems can be framed as determin- ing if two given sentences are a rewrit- ing of each other. In this paper, we pro- pose a class of kernel functions, referred to as type-enriched string rewriting ker- nels, which, used in kernel-based machine learning …...

Most previous work of text normalization on informal text made a strong assumption that the system has already known which tokens are non-standard words (NSW) and thus need normalization. However, this is not realistic. In this paper, we propose a method for NSW detection. In addi- tion to the information …...

In this paper, we propose a syllable-based method for tweet normalization to study the cognitive process of non-standard word creation in social media. Assuming that syllable plays a fundamental role in forming the non-standard tweet words, we choose syllable as the basic unit and extend the conventional noisy channel model …...

We investigate multiple many-to-many alignments as a primary step in integrat- ing supplemental information strings in string transduction. Besides outlining DP based solutions to the multiple alignment problem, we detail an approximation of the problem in terms of multiple sequence segmentations satisfying a coupling con- straint. We apply our approach …...

This paper presents the NL2KR platform to build systems that can translate text to different formal languages. It is freely- available1, customizable, and comes with an Interactive GUI support that is use- ful in the development of a translation system. Our key contribution is a user- friendly system based on …...

We develop an approach for generating deep (i.e, high-level) comprehension questions from novel text that bypasses the myriad challenges of creating a full se- mantic representation. We do this by de- composing the task into an ontology- crowd-relevance workflow, consisting of first representing the original text in a low-dimensional ontology, …...

Using natural language to write programs is a touchstone problem for computational linguistics. We present an approach that learns to map natural-language descrip- tions of simple “if-then” rules to executable code. By training and testing on a large cor- pus of naturally-occurring programs (called “recipes”) and their natural language de- …...

Deception detection has been formulated as a supervised binary classification prob- lem on single documents. However, in daily life, millions of fraud cases involve detailed conversations between deceivers and victims. Deceivers may dynamically adjust their deceptive statements accord- ing to the reactions of victims. In addition, people may form groups …...

The quality of statistical machine translation performed with phrase based approaches can be increased by permuting the words in the source sentences in an order which resem- bles that of the target language. We propose a class of recurrent neu- ral models which exploit source-side dependency syntax features to re- …...

We introduce into Bayesian decipherment a base distribution derived from similari- ties of word embeddings. We use Dirich- let multinomial regression (Mimno and McCallum, 2012) to learn a mapping be- tween ciphertext and plaintext word em- beddings from non-parallel data. Exper- imental results show that the base dis- tribution is …...

Modern statistical machine translation (SMT) systems usually use a linear com- bination of features to model the quality of each translation hypothesis. The linear combination assumes that all the features are in a linear relationship and constrains that each feature interacts with the rest fea- tures in an linear manner, …...

We achieve significant improvements in several syntax-based machine translation experiments using a string-to-tree vari- ant of multi bottom-up tree transducers. Our new parameterized rule extraction al- gorithm extracts string-to-tree rules that can be discontiguous and non-minimal in contrast to existing algorithms for the tree-to-tree setting. The obtained models significantly outperform …...

We present a novel framework for ma- chine translation evaluation using neural networks in a pairwise setting, where the goal is to select the better translation from a pair of hypotheses, given the reference translation. In this framework, lexical, syntactic and semantic information from the reference and the two hypotheses …...

Continuous space word embeddings learned from large, unstructured corpora have been shown to be effective at cap- turing semantic regularities in language. In this paper we replace LDA’s param- eterization of “topics” as categorical distributions over opaque word types with multivariate Gaussian distributions on the embedding space. This encourages the …...