Articles

We develop a novel embedding-based model for predicting legislative roll-call votes from bill text. The model introduces multidimen- sional ideal vectors for legislators as an alter- native to single dimensional ideal point mod- els for quantitatively analyzing roll-call data. These vectors are learned to correspond with pre-trained word embeddings which …...

In this paper, we investigate case restoration for text without case information. Previous such work operates at the word level. We pro- pose an approach using character-level recur- rent neural networks (RNN), which performs competitively compared to language model- ing and conditional random fields (CRF) ap- proaches. We further provide …...

Psychological analysis of language has repeat- edly shown that an individual’s rate of men- tioning 1st person singular pronouns predicts a wealth of important demographic and psy- chological factors. However, these analy- ses are performed out of context — syntac- tic and semantic — which may change the magnitude or …...

We investigate whether psychological well- being translates across English and Span- ish Twitter, by building and comparing source language and automatically translated weighted lexica in English and Spanish. We find that the source language models perform substantially better than the machine trans- lated versions. Moreover, manually correct- ing translation errors …...

We present an interpretable neural network ap- proach to predicting and understanding polite- ness in natural language requests. Our mod- els are based on simple convolutional neural networks directly on raw text, avoiding any manual identification of complex sentiment or syntactic features, while performing bet- ter than such feature-based models …...

Natural language understanding is the core of the human computer interactions. How- ever, building new domains and tasks that need a separate set of models is a bottle- neck for scaling to a large number of do- mains and experiences. In this paper, we propose a practical technique that addresses …...

The PPDB is an automatically built database which contains millions of paraphrases in dif- ferent languages. Paraphrases in this resource are associated with features that serve to their ranking and reflect paraphrase quality. This context-unaware ranking captures the seman- tic similarity of paraphrases but cannot serve to estimate their adequacy …...

We compare the effectiveness of four differ- ent syntactic CCG parsers for a semantic slot- filling task to explore how much syntactic su- pervision is required for downstream seman- tic analysis. This extrinsic, task-based evalua- tion also provides a unique window into the se- mantics captured (or missed) by unsupervised …...

The brain is the locus of our language abil- ity, and so brain images can be used to ground linguistic theories. Here we introduce Brain- Bench, a lightweight system for testing dis- tributional models of word semantics. We compare the performance of several models, and show that the performance on …...

We adapt the greedy stack LSTM dependency parser of Dyer et al. (2015) to support a training-with-exploration procedure using dy- namic oracles (Goldberg and Nivre, 2013) in- stead of assuming an error-free action his- tory. This form of training, which accounts for model predictions at training time, improves parsing accuracies. …...

We propose a novel unsupervised word align- ment method that uses a constraint based on Inversion Transduction Grammar (ITG) parse trees to jointly unify two directional mod- els. Previous agreement methods are not helpful for locating alignments with long dis- tances because they do not use any syntactic structures. In …...

We introduce a recurrent neural network lan- guage model (RNN-LM) with long short- term memory (LSTM) units that utilizes both character-level and word-level inputs. Our model has a gate that adaptively finds the op- timal mixture of the character-level and word- level inputs. The gate creates the final vec- tor …...

Deep neural networks have achieved remark- able results across many language processing tasks, however these methods are highly sen- sitive to noise and adversarial attacks. We present a regularization based method for lim- iting network sensitivity to its inputs, inspired by ideas from computer vision, thus learning models that are …...

We propose a method for learning the structure of variable-order CRFs, a more flexible variant of higher-order linear-chain CRFs. Variable- order CRFs achieve faster inference by in- cluding features for only some of the tag n- grams. Our learning method discovers the useful higher-order features at the same time as …...

While much computational work on fiction has focused on works in the literary canon, user-created fanfiction presents a unique op- portunity to study an ecosystem of liter- ary production and consumption, embodying qualities both of large-scale literary data (55 billion tokens) and also a social network (with over 2 million …...

Several studies on sentence processing sug- gest that the mental lexicon keeps track of the mutual expectations between words. Current DSMs, however, represent context words as separate features, thereby loosing important information for word expectations, such as word interrelations. In this paper, we present a DSM that addresses this issue …...

This paper investigates how linguistic knowl- edge mined from large text corpora can aid the generation of natural language descriptions of videos. Specifically, we integrate both a neu- ral language model and distributional seman- tics trained on large text corpora into a recent LSTM-based architecture for video descrip- tion. We …...

Recently, a number of deep-learning based models have been proposed for the task of Visual Question Answering (VQA). The per- formance of most models is clustered around 60-70%. In this paper we propose system- atic methods to analyze the behavior of these models as a first step towards recognizing their …...

We present a neural network architecture to predict a point in color space from the se- quence of characters in the color’s name. Using large scale color–name pairs obtained from an online color design forum, we eval- uate our model on a “color Turing test” and find that, given a …...

We propose an algorithm that combines su- pervised and unsupervised methods to ensem- ble multiple systems for two popular Knowl- edge Base Population (KBP) tasks, Cold Start Slot Filling (CSSF) and Tri-lingual Entity Dis- covery and Linking (TEDL). We demonstrate that it outperforms the best system for both tasks in …...