ACL 2016 论文列表

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 inference approach (Yang and Cardie, 2013) to opinion entities extraction, performing below even the standalone sequence-tagging CRF. Incorporating sentence-level and a novel relation-level optimization, however, allows the LSTM to identify opinion relations and to perform within 1– 3% of the state-of-the-art joint model for opinion entities and the IS - FROM relation; and to perform as well as the state-of-the-art for the IS - ABOUT relation — all without access to opinion lexicons, parsers and other preprocessing components required for the feature-rich CRF+ILP approach.

Several large cloze-style context-question-answer datasets have been introduced recently: the CNN and Daily Mail news data and the Children's Book Test. Thanks to the size of these datasets, the associated text comprehension task is well suited for deep-learning techniques that currently seem to outperform all alternative approaches. We present a new, simple model that uses attention to directly pick the answer from the context as opposed to computing the answer using a blended representation of words in the document as is usual in similar models. This makes the model particularly suitable for question-answering problems where the answer is a single word from the document. Ensemble of our models sets new state of the art on all evaluated datasets.

Recent years have seen a dramatic growth in the popularity of word embeddings mainly owing to their ability to capture semantic 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. In this work, we study how word embeddings can be used in Word Sense Disambiguation, one of the oldest tasks in Natural Language Processing and Artificial Intelligence. We propose different methods through which word embeddings can be leveraged in a state-of-the-art supervised WSD system architecture, and perform a deep analysis of how different parameters affect performance. We show how a WSD system that makes use of word embeddings alone, if designed properly, can provide significant performance improvement over a state-ofthe-art WSD system that incorporates several standard WSD features.

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 problem types. Problems in the dataset are semi-automatically obtained from community question-answering (CQA) web pages. A ranking SVM model is trained to automatically extract problem answers from the answer text provided by CQA users, which significantly reduces human annotation cost. Experiments conducted on the new dataset lead to interesting and surprising results.

We prove that log-linearly interpolated backoff language models can be efficiently and exactly collapsed into a single normalized backoff model, contradicting Hsu (2007). While prior work reported that log-linear interpolation yields lower per-plexity than linear interpolation, normalizing at query time was impractical. We normalize the model offline in advance, which is efficient due to a recurrence relationship between the normalizing factors. To tune interpolation weights, we apply Newton’s method to this convex problem and show that the derivatives can be computed ef-ficiently in a batch process. These findings are combined in new open-source interpolation tool, which is distributed with KenLM. With 21 out-of-domain corpora, log-linear interpolation yields 72.58 per-plexity on TED talks, compared to 75.91 for linear interpolation.

Our goal is to generate reading lists for students that help them optimally learn technical 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 concepts that must be understood before reading material pertaining to a given query is never considered. Here we formulate an information-theoretic view of concept dependency and present methods to construct a “concept graph” automatically from a text corpus. We perform the first human evaluation of concept dependency edges (to be published as open data), and the results verify the feasibility of automatic approaches for inferring concepts and their dependency relations. This result can support search capabilities that may be tuned to help users learn a subject rather than retrieve documents based on a single query.

Motivated by the findings in social science that people’s opinions are diverse and variable while together they are shaped by evolving social norms, we perform personalized sentiment classification via shared model adaptation over time. In our proposed solution, a global sentiment model is constantly updated to capture the homogeneity in which users express opinions, while personalized models are simultaneously adapted from the global model to recognize the heterogeneity of opinions from individuals. Global model sharing alleviates data sparsity issue, and individualized model adaptation enables ef-ficient online model learning. Extensive experimentations are performed on two large review collections from Amazon and Yelp, and encouraging performance gain is achieved against several state-of-the-art transfer learning and multi-task learning based sentiment classification solutions.

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 perfectly aligned with reality. In this paper, we conduct a large-scale crowdsourcing ex-periment on guessing age and gender from tweets. We systematically analyze the quality and possible biases of these predictions. We identify the textual cues which lead to miss-assessments of traits or make annotators more or less confident in their choice. Our study demonstrates that differences be-tween real and perceived traits are noteworthy and elucidates inaccurately used stereotypes in human perception.

Coordination is an important and common syntactic construction which is not handled well by state of the art parsers. Coordinations in the Penn Treebank are missing internal structure in many cases, do not include explicit marking of the conjuncts and contain various errors and inconsistencies. In this work, we initiated manual annotation process for solving these issues. We identify the different elements in a coordination phrase and label each element with its function. We add phrase boundaries when these are missing, unify inconsistencies, and fix errors. The outcome is an extension of the PTB that includes consistent and detailed structures for coordinations. We make the coordination annotation publicly available, in hope that they will facilitate further research into coordination disambiguation.

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 syntactic NMT model, extending a sequence-to-sequence model with the source-side phrase structure. Our model has an attention mechanism that enables the decoder to generate a translated word while softly aligning it with phrases as well as words of the source sentence. Experimental results on the WAT'15 English-to-Japanese dataset demonstrate that our proposed model considerably outperforms sequence-to-sequence attentional NMT models and compares favorably with the state-of-the-art tree-to-string SMT system.

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 challenge by setting up a multi-perspective semantic characterization of the inferred classes. To this end, we link the inferred classes and their English translation to independently constructed semantic classes in three different lexicons - the German wordnet GermaNet, VerbNet and FrameNet - and perform a detailed analysis and evaluation of the resulting German-English classification (available at www.ukp.tu-darmstadt.de/modality-verbclasses/).

How can we enable computers to automatically answer questions like "Who created the character Harry Potter"? Carefully built knowledge bases provide rich sources of facts. However, it remains a challenge to answer factoid questions raised in natural language due to numerous expressions of one question. In particular, we focus on the most common questions --- ones that can be answered with a single fact in the knowledge base. We propose CFO, a Conditional Focused neural-network-based approach to answering factoid questions with knowledge bases. Our approach first zooms in a question to find more probable candidate subject mentions, and infers the final answers with a unified conditional probabilistic framework. Powered by deep recurrent neural networks and neural embeddings, our proposed CFO achieves an accuracy of 75.7% on a dataset of 108k questions - the largest public one to date. It outperforms the current state of the art by an absolute margin of 11.8%.

This is the author accepted manuscript. The final version is available from Association for Computational Linguistics at http://acl2016.org/index.php?article_id=71.

While unsupervised anaphoric zero pro-noun (AZP) resolvers have recently been shown to rival their supervised counter-parts in performance, it is relatively difficult 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 exploiting lexical features. To address these weaknesses, we propose a supervised approach to AZP resolution based on deep neural networks, taking advantage of their ability to learn useful task-specific representations and effectively exploit lexical features via word embeddings. Our approach achieves state-of-the-art performance when resolving the Chinese AZPs in the OntoNotes corpus.

A major challenge of semantic parsing is the vocabulary mismatch problem between natural language and target ontology. In this paper, we propose a sentence rewriting based semantic parsing method, which can effectively resolve the mismatch problem by rewriting a sentence into a new form which has the same structure with its target logical form. Specifically, we propose two sentence-rewriting methods for two common types of mismatch: a dictionary-based method for 1-N mismatch and a template-based method for N-1 mismatch. We evaluate our entence rewriting based semantic parser on the benchmark semantic parsing dataset -- WEBQUESTIONS. Experimental results show that our system outperforms the base system with a 3.4% gain in F1, and generates logical forms more accurately and parses sentences more robustly.

Relation classification is an important semantic processing task in the field of natural language processing (NLP). In this paper, we present a novel model BRCNN to classify the relation of two entities in a sentence. Some state-of-the-art systems concentrate on modeling the shortest dependency path (SDP) between two entities leveraging convolutional or recurrent neural networks. We further explore how to make full use of the dependency relations information in the SDP, by combining convolutional neural networks and twochannel recurrent neural networks with long short term memory (LSTM) units. We propose a bidirectional architecture to learn relation representations with directional information along the SDP forwards and backwards at the same time, which benefits classifying the direction of relations. Experimental results show that our method outperforms the state-of-theart approaches on the SemEval-2010 Task 8 dataset.

Neuro-imaging studies on reading different parts of speech (PoS) report somewhat mixed results, yet some of them indicate different activations with different PoS. This paper addresses the difficulty of using fMRI to discriminate between linguistic tokens in reading of running text because of low temporal resolution. We show that once we solve this problem, fMRI data contains a signal of PoS distinctions to the extent that it improves PoS induction with error reductions of more than 4%.

We introduce the Treebank of Learner English (TLE), the first publicly available syntactic treebank for English as a Second Language (ESL). The TLE provides manually annotated POS tags and Universal Dependency (UD) trees for 5,124 sentences from the Cambridge First Certificate in English (FCE) corpus. The UD annotations are tied to a pre-existing error annotation of the FCE, whereby full syntactic analyses are provided for both the original and error corrected versions of each sentence. Further on, we delineate ESL annotation guidelines that allow for consistent syntactic treatment of ungrammatical English. Finally, we benchmark POS tagging and dependency parsing performance on the TLE dataset and measure the effect of grammatical errors on parsing accuracy. We envision the treebank to support a wide range of linguistic and computational research on second language acquisition as well as automatic processing of ungrammatical language. The treebank is available at universaldependencies.org. The annotation manual used in this project and a graphical query engine are available at esltreebank.org.

Digital personal assistants are becoming both more common and more useful. The major NLP challenge for personal assistants is machine understanding: translating natural language user commands into an executable representation. This paper focuses on understanding rules written as If-Then statements, though the techniques should be portable to other semantic parsing tasks. We view understanding as structure prediction and show improved models using both conventional techniques and neural network models. We also discuss various ways to improve generalization and reduce overfitting: synthetic training data from paraphrase, grammar combinations, feature selection and ensembles of multiple systems. An ensemble of these techniques achieves a new state of the art result with 8% accuracy improvement.