Articles

Coreference resolution is one of the first stages in deep language understanding and its impor- tance has been well recognized in the natu- ral language processing community. In this paper, we propose a generative, unsupervised ranking model for entity coreference resolu- tion by introducing resolution mode variables. Our unsupervised system …...

There is a significant gap between the per- formance of a coreference resolution system on gold mentions and on system mentions. This gap is due to the large and unbalanced search space in coreference resolution when using system mentions. In this paper we show that search space pruning is a …...

There is compelling evidence that corefer- ence prediction would benefit from modeling global information about entity-clusters. Yet, state-of-the-art performance can be achieved with systems treating each mention prediction independently, which we attribute to the inher- ent difficulty of crafting informative cluster- level features. We instead propose to use re- current …...

Modern NLP models rely heavily on engi- neered features, which often combine word and contextual information into complex lexi- cal features. Such combination results in large numbers of features, which can lead to over- fitting. We present a new model that repre- sents complex lexical features—comprised of parts for words, …...

Dropped Pronouns (DP) in which pronouns are frequently dropped in the source language but should be retained in the target language are challenge in machine translation. In re- sponse to this problem, we propose a semi- supervised approach to recall possibly missing pronouns in the translation. Firstly, we build training …...

Artificial neural networks are powerful mod- els, which have been widely applied into many aspects of machine translation, such as language modeling and translation mod- eling. Though notable improvements have been made in these areas, the reordering prob- lem still remains a challenge in statistical ma- chine translations. In this …...

This paper introduces information density and machine translation quality estimation in- spired features to automatically detect and classify human translated texts. We inves- tigate two settings: discriminating between translations and comparable originally au- thored texts, and distinguishing two levels of translation professionalism. Our framework is based on delexicalised sentence-level dense …...

For many low-resource languages, spoken lan- guage resources are more likely to be an- notated with translations than transcriptions. This bilingual speech data can be used for word-spotting, spoken document retrieval, and even for documentation of endangered lan- guages. We experiment with the neural, atten- tional model applied to this …...

Textual similarity measurement is a challeng- ing problem, as it requires understanding the semantics of input sentences. Most previ- ous neural network models use coarse-grained sentence modeling, which has difficulty cap- turing fine-grained word-level information for semantic comparisons. As an alternative, we propose to explicitly model pairwise word in- teractions …...

Recurrent neural networks, particularly long short-term memory (LSTM), have recently shown to be very effective in a wide range of sequence modeling problems, core to which is effective learning of distributed representation for subsequences as well as the sequences they form. An assumption in almost all the previ- ous models, …...

This paper proposes a novel translation-based knowledge graph embedding that preserves the logical properties of relations such as tran- sitivity and symmetricity. The embedding space generated by existing translation-based embeddings do not represent transitive and symmetric relations precisely, because they ignore the role of entities in triples. Thus, we introduce …...

Can crowdsourced annotation of training data boost performance for relation extraction over methods based solely on distant supervision? While crowdsourcing has been shown effec- tive for many NLP tasks, previous researchers found only minimal improvement when ap- plying the method to relation extraction. This paper demonstrates that a much larger …...

Universal schema builds a knowledge base (KB) of entities and relations by jointly embedding all re- lation types from input KBs as well as textual pat- terns observed in raw text. In most previous appli- cations of universal schema, each textual pattern is represented as a single embedding, preventing gen- …...

Neural encoder-decoder models of machine translation have achieved impressive results, rivalling traditional translation models. How- ever their modelling formulation is overly simplistic, and omits several key inductive bi- ases built into traditional models. In this paper we extend the attentional neural translation model to include structural biases from word based …...

We propose multi-way, multilingual neural machine translation. The proposed approach enables a single neural translation model to translate between multiple languages, with a number of parameters that grows only lin- early with the number of languages. This is made possible by having a single atten- tion mechanism that is shared …...

We address the problem of automatically finding the parameters of a statistical ma- chine translation system that maximize BLEU scores while ensuring that decoding speed ex- ceeds a minimum value. We propose the use of Bayesian Optimization to efficiently tune the speed-related decoding parameters by eas- ily incorporating speed as …...

Representation and learning of commonsense knowledge is one of the foundational prob- lems in the quest to enable deep language un- derstanding. This issue is particularly chal- lenging for understanding casual and corre- lational relationships between events. While this topic has received a lot of interest in the NLP community, …...

We propose a novel neural network model for machine reading, DER Network, which ex- plicitly implements a reader building dynamic meaning representations for entities by gath- ering and accumulating information around the entities as it reads a document. Eval- uated on a recent large scale dataset (Her- mann et al., …...

We address relation classification in the con- text of slot filling, the task of finding and eval- uating fillers like “Steve Jobs” for the slot X in “X founded Apple”. We propose a convo- lutional neural network which splits the input sentence into three parts according to the re- lation …...

In recent years many knowledge bases (KBs) have been constructed, yet there is not yet a verb resource that maps to these growing KB resources. A resource that maps verbs in different languages to KB relations would be useful for extracting facts from text into the KBs, and to aid …...