Articles

We present the Siamese Continuous Bag of Words (Siamese CBOW) model, a neural network for efficient estimation of high- quality sentence embeddings. Averaging the embeddings of words in a sentence has proven to be a surprisingly success- ful and efficient way of obtaining sen- tence embeddings. However, word em- beddings …...

This paper proposes a left-corner parser which can identify nonlocal dependencies. Our parser integrates nonlocal dependency identification into a transition-based sys- tem. We use a structured perceptron which enables our parser to utilize global features captured by nonlocal dependencies. An experimental result demonstrates that our parser achieves a good balance …...

We investigate the use of deep bi- directional LSTMs for joint extraction of opinion entities and the IS-FROM and IS- ABOUT relations that connect them — the first such attempt using a deep learning approach. Perhaps surprisingly, we find that standard LSTMs are not competitive with a state-of-the-art CRF+ILP joint …...

Several large cloze-style context-question- answer datasets have been introduced re- cently: the CNN and Daily Mail news data and the Children’s Book Test. Thanks to the size of these datasets, the asso- ciated text comprehension task is well suited for deep-learning techniques that currently seem to outperform all alterna- tive …...

Recent years have seen a dramatic growth in the popularity of word embeddings mainly owing to their ability to capture se- mantic information from massive amounts of textual content. As a result, many tasks in Natural Language Processing have tried to take advantage of the potential of these distributional models. …...

Can we train a system that, on any new input, either says “don’t know” or makes a prediction that is guaranteed to be cor- rect? We answer the question in the affir- mative provided our model family is well- specified. Specifically, we introduce the unanimity principle: only predict when all …...

Deep Random Walk (DeepWalk) can learn a latent space representation for describ- ing the topological structure of a network. However, for relational network classifi- cation, DeepWalk can be suboptimal as it lacks a mechanism to optimize the ob- jective of the target task. In this paper, we present Discriminative Deep …...

Recently a few systems for automatically solving math word problems have reported promising results. However, the datasets used for evaluation have limitations in both scale and diversity. In this paper, we build a large-scale dataset which is more than 9 times the size of previous ones, and contains many more …...

We prove that log-linearly interpolated backoff language models can be efficiently and exactly collapsed into a single nor- malized backoff model, contradicting Hsu (2007). While prior work reported that log-linear interpolation yields lower per- plexity than linear interpolation, normaliz- ing at query time was impractical. We nor- malize the model …...

Our goal is to generate reading lists for stu- dents that help them optimally learn techni- cal material. Existing retrieval algorithms return items directly relevant to a query but do not return results to help users read about the concepts supporting their query. This is because the dependency structure of …...

Fill-in-the-blank items are commonly fea- tured in computer-assisted language learn- ing (CALL) systems. An item displays a sentence with a blank, and often proposes a number of choices for filling it. These choices should include one correct answer and several plausible distractors. We de- scribe a system that, given an …...

Motivated by the findings in social sci- ence that people’s opinions are diverse and variable while together they are shaped by evolving social norms, we perform person- alized sentiment classification via shared model adaptation over time. In our pro- posed solution, a global sentiment model is constantly updated to capture …...

User traits disclosed through written text, such as age and gender, can be used to per- sonalize applications such as recommender systems or conversational agents. However, human perception of these traits is not per- fectly aligned with reality. In this paper, we conduct a large-scale crowdsourcing ex- periment on guessing …...

Coordination is an important and common syntactic construction which is not han- dled well by state of the art parsers. Co- ordinations in the Penn Treebank are miss- ing internal structure in many cases, do not include explicit marking of the conjuncts and contain various errors and inconsisten- cies. In …...

Most of the existing Neural Machine Translation (NMT) models focus on the conversion of sequential data and do not directly use syntactic information. We propose a novel end-to-end syntac- tic NMT model, extending a sequence- to-sequence model with the source-side phrase structure. Our model has an at- tention mechanism that …...

We revisit Levin’s theory about the correspondence of verb meaning and syntax and infer semantic classes from a large syntactic classification of more than 600 German verbs taking clausal and non-finite arguments. Grasping the meaning components of Levin-classes is known to be hard. We address this chal- lenge by setting …...

How can we enable computers to automat- ically answer questions like “Who created the character Harry Potter”? Carefully built knowledge bases provide rich sources of facts. However, it remains a chal- lenge to answer factoid questions raised in natural language due to numerous ex- pressions of one question. In particular, …...

Supervised machine learning models for automated essay scoring (AES) usually re- quire substantial task-specific training data in order to make accurate predictions for a particular writing task. This limita- tion hinders their utility, and consequently their deployment in real-world settings. In this paper, we overcome this shortcoming using a constrained …...

While unsupervised anaphoric zero pro- noun (AZP) resolvers have recently been shown to rival their supervised counter- parts in performance, it is relatively diffi- cult to scale them up to reach the next level of performance due to the large amount of feature engineering efforts involved and their ineffectiveness in …...

A major challenge of semantic parsing is the vocabulary mismatch problem be- tween natural language and target ontol- ogy. In this paper, we propose a sen- tence rewriting based semantic parsing method, which can effectively resolve the mismatch problem by rewriting a sentence into a new form which has the …...