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we propose using source-language monolingual models and resources to paraphrase the source text prior to translation---we propose an approach that consists in directly replacing unknown source terms , using source-language resources and models | 1 |
we present a graph-based semi-supervised learning for the question-answering ( qa ) task for ranking candidate sentences---in this paper , we applied a graph-based ssl algorithm to improve the performance of qa task | 1 |
hu et al proposes integration of constraints coming in the form of first order logic rules during training of nns---hu et al enabled a neural network to learn simultaneously from labeled instances as well as logic rules | 1 |
the system used a tri-gram language model built from sri toolkit with modified kneser-ney interpolation smoothing technique---the target language model was a trigram language model with modified kneser-ney smoothing trained on the english side of the bitext using the srilm tookit | 1 |
the proposed rnns approach achieved a performance comparable to the existing state-of-the-art models at sentence-level qe---we measure the translation quality with automatic metrics including bleu and ter | 0 |
this shows that rl is possible even from small amounts of fairly reliable human feedback , pointing to a great potential for applications at larger scale---in machine translation proportions , this result points towards a great potential for larger-scaler applications of rl from human feedback | 1 |
for learning coreference decisions , we used a maximum entropy model---as a model learning method , we adopt the maximum entropy model learning method | 1 |
the word vectors used in all approaches are taken from the word2vec google news model---the word embedding vectors are generated from word2vec over the 5th edition of the gigaword | 1 |
neural networks have recently gained much attention as a way of inducing word vectors---neural models , with various neural architectures , have recently achieved great success | 1 |
all tokens are first mapped to distributed word representations , pre-trained using word2vec on the google news corpus---this baseline uses pre-trained word embeddings using word2vec cbow and fasttext | 1 |
the baseline of our approach is a statistical phrase-based system which is trained using moses---the feature weights are tuned to optimize bleu using the minimum error rate training algorithm | 0 |
a 5-gram language model was built using srilm on the target side of the corresponding training corpus---the target-side language models were estimated using the srilm toolkit | 1 |
in this work we use word embeddings of mikolov et al to represent the semantics of words and compounds---we adapt the models of mikolov et al and mikolov et al to infer feature embeddings | 1 |
we build a french tagger based on englishfrench data from the europarl corpus---we use the europarl english-french parallel corpus plus around 1m segments of symantec translation memory | 1 |
we present a visual-linguistic mapping for zsl in the case where words and visual categories are both represented by distributions---in this paper , we consider zsl in the case where both visual and linguistic concepts are represented by gaussian distribution | 1 |
for the mix one , we also train word embeddings of dimension 50 using glove---we use pre-trained vectors from glove for word-level embeddings | 1 |
compared with 2nd-order phrase model of pei et al , our basic model occasionally performs worse in recovering long distant dependencies---for the classification task , we use pre-trained glove embedding vectors as lexical features | 0 |
we use the publicly available 300-dimensional word vectors of mikolov et al , trained on part of the google news dataset---we follow mikolov et al to use skip-gram based word2vec to compute embeddings , and conduct training on the english articles in the latest 2015 wikipedia dump | 1 |
we exploit the wikipedia section structure to generate a large dataset of weakly labeled triplets of sentences with no human involvement---component gathers lexical statistics from an unannotated corpus of newswire text | 0 |
first-order factoid question answering assumes that the question can be answered by a single fact in a knowledge base ( kb )---question answering ( qa ) assumes that the question can be answered by a single fact in a knowledge base ( kb ) | 1 |
word vector models are a good way of modelling lexical semantics , since they are robust , conceptually simple and mathematically well defined---we created a data collection for research , development and evaluation of a method for automatically answering why-questions ( why-qa ) | 0 |
we use publicly-available 1 300-dimensional embeddings trained on part of the google news dataset using skip-gram with negative sampling---we initialize our word representation using publicly available word2vec trained on google news dataset and keep them fixed during training | 1 |
a 3-gram language model was trained from the target side of the training data for chinese and arabic , using the srilm toolkit---the target language model was a trigram language model with modified kneser-ney smoothing trained on the english side of the bitext using the srilm tookit | 1 |
notice that the 2 } 3 } fan-out of the non-terminal math-w-7-15-1-87 is 2---in the value of the input fan-out bound math-w-19-1-0-19 | 1 |
in this paper , we present an implicit content-introducing method for generative conversation systems , which incorporates cue words using our proposed hierarchical gated fusion unit ( hgfu ) in a flexible way---unlike the existing work , we explore an implicit content-introducing method for neural conversation systems... | 1 |
event extraction is a task in information extraction where mentions of predefined events are extracted from texts---event extraction is a particularly challenging information extraction task , which intends to identify and classify event triggers and arguments from raw text | 1 |
the language model has an embedding size of 250 and two lstm layers with a hidden size of 1000---in this paper , we perform an analysis of the human perceptions of edit importance while reviewing documents | 0 |
we make use of a factorization model in which words , together with their window-based context words and their dependency relations , are linked to latent dimensions---arabic is a morphologically rich language , in which a word carries not only inflections but also clitics , such as pronouns , conjunctions , and prepos... | 0 |
we use minimum error rate training to tune the feature weights of hpb for maximum bleu score on the development set with serval groups of different start weights---we use 4-gram language models in both tasks , and conduct minimumerror-rate training to optimize feature weights on the dev set | 1 |
as dependency relations directly model the semantics structure of a sentence , shen et al introduce dependency language model to better account for the generation of target sentences---in this section , we describe the observed data , latent variables , and auxiliary variables of the problem | 0 |
sentiment analysis is the process of identifying and extracting subjective information using natural language processing ( nlp )---in order to provide word clusters for our experiments , we used the brown clustering algorithm | 0 |
classifiers trained with features constructed from our model achieve significant better predictive performance than the state-of-the-art---classifiers trained with our features significantly outperform the state-of-the-art results | 1 |
chen et al propose gated recursive neural networks , a variant of grconvs , to solve chinese word segmentation problem---we use glove vectors with 100 dimensions trained on wikipedia and gigaword as word embeddings | 0 |
the target-normalized hierarchical phrase-based model is based on a more general hierarchical phrase-based model---the hierarchical model is built on a weighted synchronous contextfree grammar | 1 |
our learned models of the best wizard¡¯s behavior combine features available to wizards with some that are not , such as recognition confidence and acoustic model scores---our learned models of the best wizard ¡¯ s behavior combine features that are available to wizards with some that are not , such as recognition conf... | 1 |
kaji and kitsuregawa outline a method of building sentiment lexicons for japanese using structural cues from html documents---kaji and kitsuregawa propose a method for building sentiment lexicon for japanese from html pages | 1 |
we used kenlm with srilm to train a 5-gram language model based on all available target language training data---we created 5-gram language models for every domain using srilm with improved kneserney smoothing on the target side of the training parallel corpora | 1 |
the training and development data for our task was taken from prior work on twitter ner , which distinguishes 10 different named entity types---the training and development data for our task was taken from previous work on twitter ner , which distinguishes 10 different named entity types | 1 |
we use the hierarchical phrase-based machine translation model from the open-source cdec toolkit , and datasets from the workshop on machine translation---we propose a new bridging operation that generates predicates based on adjacent predicates | 0 |
socher et al model the two sentences with recursive neural networks , and then feed similarity scores between words and phrases to a cnn with dynamic pooling to capture sentence interactions---socher et al utilized parsing to model the hierarchical structure of sentences and uses unfolding recursive autoencoders to lea... | 1 |
the information can be adjusted concretely by hand in each case of incorrect analysis---information can be obtained from cases where the system incorrectly analyzes sentences | 1 |
the language model pis implemented as an n-gram model using the srilm-toolkit with kneser-ney smoothing---a 4-gram language model is trained on the monolingual data by srilm toolkit | 1 |
the model parameters are trained using minimum error-rate training---coreference resolution is the task of determining whether two or more noun phrases refer to the same entity in a text | 0 |
in addition , we explore methods to improve phrase structure parsing for learner english---in this paper , we first propose a phrase structure annotation scheme for learner english | 1 |
we propose a novel text normalization model based on learning edit operations from labeled data while incorporating features induced from unlabeled data via character-level neural text embeddings---we suggest a simple , supervised character-level string transduction model which easily incorporates features automaticall... | 1 |
the evaluation protocol and metrics were very similar to which allowed us to do indirect comparison to previous work---ten of these concepts were identical to ones used in , which allowed us to compare our results to recent work in case of english | 1 |
for estimating monolingual word vector models , we use the cbow algorithm as implemented in the word2vec package using a 5-token window---word representations to learn word embeddings from our unlabeled corpus , we use the gensim im-plementation of the word2vec algorithm | 1 |
the results show that srl information is very helpful for orl , which is consistent with previous studies---results show that srl is highly effective for orl , which is consistent with previous findings | 1 |
word sense disambiguation ( wsd ) is a task to identify the intended sense of a word based on its context---word sense disambiguation ( wsd ) is the task of determining the meaning of an ambiguous word in its context | 1 |
we use pretrained 300-dimensional english word embeddings---for dependency parsing , the performance improves log-linearly with the number of parameters ( unique n-grams ) | 0 |
koehn and schroeder described a procedure for domain adaptation that was using two translation models in decoding , one trained on in-domain data and the other on out-of-domain data---koehn and schroeder investigated domain adaptations by integrating in-domain and out-of-domain language models as log-linear features in... | 1 |
we induce a topic-based vector representation of sentences by applying the latent dirichlet allocation method---a few recent studies have highlighted the potential and importance of developing paraphrase identification and semantic similarity techniques specifically for tweets | 0 |
experimental results on real-world datasets show that our model achieves significant and consistent improvements on relation extraction as compared with baselines---experimental results on real-world datasets show that , our model can make full use of those sentences containing only one target entity , and achieves sig... | 1 |
this paper presents a graph-theoretic model of the acquisition of lexical syntactic representations---hence we use the expectation maximization algorithm for parameter learning | 0 |
crucially , our approach combines the strengths of entity-mention models and mention-ranking models---that combines the strengths of mention rankers and entity-mention models | 1 |
most recently , mcdonald et al investigate a structured model for jointly classifying the sentiment of text at varying levels of granularity---training is done through stochastic gradient descent over shuffled mini-batches with adadelta update rule | 0 |
the proposed models empirically show consistent improvement over the previous methods in both the bleu and err evaluation metrics---the weights of the different feature functions were optimised by means of minimum error rate training | 0 |
we were able to show that performance improves with increased depth , using up to 29 convolutional layers---and we were able to show that increasing the depth up to 29 convolutional layers steadily improves performance | 1 |
each translation model is tuned using mert to maximize bleu---semantic role labeling ( srl ) is a task of analyzing predicate-argument structures in texts | 0 |
all annotations were done using the brat rapid annotation tool---all annotations were carried out with the brat rapid annotation tool | 1 |
for our purpose we use word2vec embeddings trained on a google news dataset and find the pairwise cosine distances for all words---lin and pantel use a standard monolingual corpus to generate paraphrases , based on dependancy graphs and distributional similarity | 0 |
we used the treetagger for lemmatisation as well as part-of-speech tagging---given such parallel data , we can easily train an encoder-decoder model that takes a sentence and target syntactic template | 0 |
relation extraction ( re ) is the process of generating structured relation knowledge from unstructured natural language texts---relation extraction is a fundamental task in information extraction | 1 |
we present a graph algorithm that decides satisfiability of normal dominance constraints in polynomial time---we identify the natural fragment of normal dominance constraints and show that its satisfiability problem is in deterministic polynomial time | 1 |
previous research has shown the usefulness of using pretrained word vectors to improve the performance of various models---recent works reveal that modifying word vectors during training could capture polarity information for the sentiment words effectively | 1 |
we use the selectfrommodel 4 feature selection method as implemented in scikit-learn---for the svm classifier we use the python scikitlearn library | 1 |
the dmv is a singlestate head automata model over lexical word classes -pos tags---the dmv is a singlestate head automata model which is based on pos tags | 1 |
to train a crf model , we use the wapiti sequence labelling toolkit---we use wapiti tagger to train a standard crf tagger with iob tags for phrase chunking | 1 |
a 5-gram language model with kneser-ney smoothing was trained with srilm on monolingual english data---the language models in this experiment were trigram models with good-turing smoothing built using srilm | 1 |
in this paper , we propose a new space and a new metric for computing this distance---in this paper , we proposed a noisy-channel model for qa that can accommodate within a unified framework | 1 |
morante and daelemans use the bioscope corpus to approach the problem of identifying cues and scopes via supervised machine learning---morante and daelemans present a machine-learning approach to this task , using token-level , lexical information only | 1 |
we obtained the pos tags and parse trees of the sentences in our datasets with the stanford pos tagger and the stanford parser---we parse the source sentences using the stanford corenlp parser and linearize the resulting parses | 1 |
we use srilm to build 5-gram language models with modified kneser-ney smoothing---the language model is a large interpolated 5-gram lm with modified kneser-ney smoothing | 1 |
phrasebased smt models are tuned using minimum error rate training---the model weights are automatically tuned using minimum error rate training | 1 |
stephanie seneff tina : a natural language system for spoken language applications anisms---tina : a natural language system for spoken language applications | 1 |
the primary requirement ( and challenge ) here is to deal with multi-membership , i.e. , one item may belong to multiple different semantic classes---here is dealing with multi-membership : an item may belong to multiple semantic classes ; and we need to discover as many as possible the different semantic classes | 1 |
the bleu score for all the methods is summarised in table 5---table 1 shows the performance for the test data measured by case sensitive bleu | 1 |
given the basic nature of the semantic classes and word sense disambiguation algorithms used , we think there is ample room for future improvements---given the basic nature of the semantic classes and wsd algorithms , we think there is room for future improvements | 1 |
dependency parsing is a way of structurally analyzing a sentence from the viewpoint of modification---dependency parsing is the task of predicting the most probable dependency structure for a given sentence | 1 |
event schema induction is the task of learning a representation of events ( e.g. , bombing ) and the roles involved in them ( e.g , victim and perpetrator )---we evaluated the translation quality using the bleu-4 metric | 0 |
at the sub-sentential level , munteanu and marcu extracted sub-sentential translation pairs from comparable corpora based on the loglikelihood-ratio of word translation probability---in this paper , we propose elden , an el system which increases nodes and edges of the kg | 0 |
this is often measured by correlation with human judgment---peters et al propose a deep neural model that generates contextual word embeddings which are able to model both language and semantics of word use | 0 |
bandyopadhyay et al , 2011 , and sentiment analysis---bandyopadhyay et al , 2011 , sentiment analysis , and many other applications | 1 |
we train the cbow model with default hyperparameters in word2vec---for feature building , we use word2vec pre-trained word embeddings | 1 |
relation extraction is the task of finding relationships between two entities from text---relation extraction is a fundamental step in many natural language processing applications such as learning ontologies from texts ( cite-p-12-1-0 ) and question answering ( cite-p-12-3-6 ) | 1 |
the process of identifying the correct meaning , or sense of a word in context , is known as word sense disambiguation ( wsd )---word sense disambiguation ( wsd ) is the task of determining the meaning of a word in a given context | 1 |
we use the adam optimizer and mini-batch gradient to solve this optimization problem---relation extraction is the task of automatically detecting occurrences of expressed relations between entities in a text and structuring the detected information in a tabularized form | 0 |
relation extraction is the task of detecting and classifying relationships between two entities from text---relation extraction is a fundamental step in many natural language processing applications such as learning ontologies from texts ( cite-p-12-1-0 ) and question answering ( cite-p-12-3-6 ) | 1 |
caseinsensitive bleu is used to evaluate the translation results---for evaluation , caseinsensitive nist bleu is used to measure translation performance | 1 |
research is a collaborative effort to increase knowledge---the research described here is a further development of several strands of previous research | 1 |
to cope with the unit problem , we propose a character-based chunking method---in which we perform the chunking process based on character units | 1 |
reduction can significantly improve the conciseness of automatic summaries---reduction system can improve the conciseness of generated summaries significantly | 1 |
our translation model is implemented as an n-gram model of operations using the srilm toolkit with kneser-ney smoothing---we use srilm for training the 5-gram language model with interpolated modified kneser-ney discounting | 1 |
for automated scoring of unrestricted spontaneous speech , speech proficiency has been evaluated primarily on aspects of pronunciation , fluency , vocabulary and language usage but not on aspects of content and topicality---for automated scoring of unrestricted , spontaneous speech , most automated systems have estimat... | 1 |
we used a 5-gram language model trained on 126 million words of the xinhua section of the english gigaword corpus , estimated with srilm---wikipedia is a resource of choice exploited in many nlp applications , yet we are not aware of recent attempts to adapt coreference resolution to this resource | 0 |
the promt smt system is based on the moses open-source toolkit---the smt baseline system is built upon the opensource mt toolkit moses 9 | 1 |
a 4-gram language model was trained on the monolingual data by the srilm toolkit---our results show that the visual model outperforms the language-only model | 0 |
summarization is the process of condensing a source text into a shorter version while preserving its information content---summarization is the task of condensing a piece of text to a shorter version that contains the main information from the original | 1 |
we use the word2vec skip-gram model to learn initial word representations on wikipedia---to get a dictionary of word embeddings , we use the word2vec tool 2 and train it on the chinese gigaword corpus | 1 |
psycholinguistic experiments have shown that eye gaze is tightly linked to human language processing---work has also shown that eye gaze has a potential to improve reference resolution | 1 |
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