Input: In this paper, we present a new sequence-to-sequence pre-training model called ProphetNet, which introduces a novel self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism.


Input: Instead of the optimization of one-step ahead prediction in traditional sequence-to-sequence model, the ProphetNet is optimized by n-step ahead prediction which predicts the next n tokens simultaneously based on previous context tokens at each time step.


Input: Instead of the optimization of one-step ahead prediction in traditional sequence-to-sequence model, the ProphetNet is optimized by n-step ahead prediction which predicts the next n tokens simultaneously based on previous context tokens at each time step.


Input: We pre-train ProphetNet using a base scale dataset (16GB) and a large scale dataset (160GB)  respectively.


Input: We pre-train ProphetNet using a base scale dataset (16GB) and a large scale dataset (160GB)  respectively.


Input: While this approach underperforms its supervised counterpart, we show in this work that the two ideas can be combined: We introduce Pattern-Exploiting Training (PET), a semi-supervised training procedure that reformulates input examples as cloze-style phrases which help the language model understand the given task.


Input: Theses phrases are then used to assign soft labels to a large set of unlabeled examples.


Input: On several tasks, we show that PET outperforms both supervised training and unsupervised approaches in low-resource settings by a large margin.


Input: Based on the general linguistic structure of humor, in this paper, we propose a novel approach for detecting humor in short texts by using BERT sentence embedding.


Input: Based on the general linguistic structure of humor, in this paper, we propose a novel approach for detecting humor in short texts by using BERT sentence embedding.


Input: Our proposed method uses BERT to generate embeddings for sentences of a given text and uses these embeddings as inputs for parallel lines of hidden layers in a neural network.


Input: Our 8-layer model with 110M parameters outperforms all baseline models with a large margin, showing the importance of utilizing linguistic structure  in machine learning models.


Input: Our 8-layer model with 110M parameters outperforms all baseline models with a large margin, showing the importance of utilizing linguistic structure  in machine learning models.


Input: The Arabic language is a morphological rich language, posing many challenges for information extraction (IE) tasks, including Named Entity Recognition (NER), Part-of-Speech tagging (POS), Argument Role Labeling (ARL)  and Relation Extraction (RE).


Input: The Arabic language is a morphological rich language, posing many challenges for information extraction (IE) tasks, including Named Entity Recognition (NER), Part-of-Speech tagging (POS), Argument Role Labeling (ARL)  and Relation Extraction (RE).


Input: In this work, we pre-train a Gigaword-based bilingual language model (GigaBERT) to study these two distant languages as well as zero-short transfer learning on the information extraction tasks.


Input: Our GigaBERT model can outperform mBERT and XLM-R-base on NER, POS and ARL tasks, with regarding to the per-language and /or zero-transfer performance.We make our pre-trained models publicly available at URL to facilitate the research of this field.


Input: We make our pre-trained models publicly available at URL to facilitate the research of this field.


Input: We make our pre-trained models publicly available at URL to facilitate the research of this field.


Input: In adversarial (challenge) testing, we pose hard generalization tasks in order to gain insights into the solutions found by our models.


Input: What properties must a system have in order to succeed at these hard tasks? In this paper, we argue that an essential factor is the ability to form modular representations.


Input: In this paper, we argue that an essential factor is the ability to form modular representations.


Input: Our central contribution is a definition of what it means for a representation to be modular and an experimental method for assessing the extent to which a system's solution is modular in this general sense.


Input: We find that a BERT model with fine-tuning is strikingly successful at the hard generalization tasks we pose using this dataset, and our active manipulations help us to understand why: despite the densely interconnected nature of the BERT architecture, the learned model embeds modular, general theories of lexical entailment relations.


Input: We find that a BERT model with fine-tuning is strikingly successful at the hard generalization tasks we pose using this dataset, and our active manipulations help us to understand why: despite the densely interconnected nature of the BERT architecture, the learned model embeds modular, general theories of lexical entailment relations.


Input: We find that a BERT model with fine-tuning is strikingly successful at the hard generalization tasks we pose using this dataset, and our active manipulations help us to understand why: despite the densely interconnected nature of the BERT architecture, the learned model embeds modular, general theories of lexical entailment relations.


Input: For the automatic evaluation of Generative Question Answering (genQA) systems, it is essential to assess the correctness of the generated answers.


Input: For the automatic evaluation of Generative Question Answering (genQA) systems, it is essential to assess the correctness of the generated answers.


Input: For the automatic evaluation of Generative Question Answering (genQA) systems, it is essential to assess the correctness of the generated answers.


Input: For the automatic evaluation of Generative Question Answering (genQA) systems, it is essential to assess the correctness of the generated answers . However, n-gram similarity metrics, which are widely used to compare generated texts and references, are prone to misjudge fact-based assessments.


Input: Moreover, there is a lack of benchmark datasets to measure the quality of metrics in terms of the correctness.


Input: Moreover, there is a lack of benchmark datasets to measure the quality of metrics in terms of the correctness.


Input: To study a better metric for genQA, we collect high-quality human judgments of correctness on two standard genQA datasets.


Input: To study a better metric for genQA, we collect high-quality human judgments of correctness on two standard genQA datasets.


Input: Using our human-evaluation datasets, we show that existing metrics based on n-gram similarity  do not correlate with human judgments.


Input: Using our human-evaluation datasets, we show that existing metrics based on n-gram similarity  do not correlate with human judgments.


Input: To alleviate this problem, we propose a new metric for evaluating the correctness of genQA . Specifically, the new metric assigns different weights on each token via keyphrase prediction, thereby judging whether a predicted answer sentence captures the key meaning of the human judge's ground-truth.


Input: To alleviate this problem, we propose a new metric for evaluating the correctness of genQA . Specifically, the new metric assigns different weights on each token via keyphrase prediction, thereby judging whether a predicted answer sentence captures the key meaning of the human judge's ground-truth.


Input: To alleviate this problem, we propose a new metric for evaluating the correctness of genQA . Specifically, the new metric assigns different weights on each token via keyphrase prediction, thereby judging whether a predicted answer sentence captures the key meaning of the human judge's ground-truth.


Input: To alleviate this problem, we propose a new metric for evaluating the correctness of genQA . Specifically, the new metric assigns different weights on each token via keyphrase prediction, thereby judging whether a predicted answer sentence captures the key meaning of the human judge's ground-truth.


Input: To alleviate this problem, we propose a new metric for evaluating the correctness of genQA . Specifically, the new metric assigns different weights on each token via keyphrase prediction, thereby judging whether a predicted answer sentence captures the key meaning of the human judge's ground-truth.


Input: Our proposed metric shows a significantly higher correlation with human judgment than widely used existing metrics.


Input: Vision, as a central component of human perception, plays a fundamental role in shaping natural language . To better understand how text models are connected to our visual perceptions, we propose a method for examining the similarities between neural representations extracted from words in text and objects in images.


Input: Our approach uses a lightweight probing model that learns to map language representations of concrete words to the visual domain.


Input: Our approach uses a lightweight probing model that learns to map language representations of concrete words to the visual domain.


Input: Finally, we show that humans significantly outperform all examined models, suggesting considerable room for improvement in representation learning and grounding.


Input: In this work, we propose a method for characterizing how language representations of concrete nouns  relate to the physical appearance of the objects they refer to.


Input: In this work, we propose a method for characterizing how language representations of concrete nouns  relate to the physical appearance of the objects they refer to.


Input: We show evidence of a surprising common ground with the visual domain, finding representations of many language models to be useful in retrieving semantically aligned image patches.


Input: We show evidence of a surprising common ground with the visual domain, finding representations of many language models to be useful in retrieving semantically aligned image patches . In control experiments where language and visual representations are intentionally mismatched, we observe much weaker results. Furthermore, we examine the impact of textual context in our experiments, finding, for instance, that nouns accompanied by adjectives lead to more accurate retrieval. Finally, we show that the examined models substantially under-perform humans in retrieval .


Input: Altogether, our findings shed new empirical insights on language grounding, suggesting that some physical properties are being captured by trained language models, and highlighting large room for future progress.


Input: While large-scale contextual language models have enjoyed great success recently, much remains to be understood about what is encoded in their representations.


Input: While large-scale contextual language models have enjoyed great success recently, much remains to be understood about what is encoded in their representations.


Input: In this work, we characterize how contextual representations of concrete nouns extracted by trained language models relate to the physical properties of the objects they refer to.


Input: In this work, we characterize how contextual representations of concrete nouns extracted by trained language models relate to the physical properties of the objects they refer to. Our approach uses a probing model that examines how effective these language representations are in discerning between different visual representations.


Input: Our approach uses a probing model that examines how effective these language representations are in discerning between different visual representations.


Input: Much weaker results are found in control experiments, attesting the selectivity of the probe. All examined models greatly under-perform humans in retrieval, highlighting substantial room for future progress.


Input: All examined models greatly under-perform humans in retrieval, highlighting substantial room for future progress. Altogether, our findings shed new empirical insights on language grounding and its materialization in contextual language models.


Input: We focus on the study of conversational recommendation in the context of multi-type dialogs, where the bots can proactively and naturally lead a conversation from a non-recommendation dialog (e.g., QA) to a recommendation dialog, taking into account user's interests and feedback.


Input: We focus on the study of conversational recommendation in the context of multi-type dialogs, where the bots can proactively and naturally lead a conversation from a non-recommendation dialog (e.g., QA) to a recommendation dialog, taking into account user's interests and feedback.


Input: To facilitate the study of this task, we create a human-to-human Chinese dialog dataset DuRecDial (about 10k dialogs, 156k utterances), which contains multiple sequential dialogs for every pair of a recommendation seeker (user) and a recommender (bot).


Input: However, it is often overlooked that the decoder only has a single view of the source sequences, that is, the representations generated by the last encoder layer, which is supposed to be a  global view of source sequences.


Input: However, it is often overlooked that the decoder only has a single view of the source sequences, that is, the representations generated by the last encoder layer, which is supposed to be a  global view of source sequences.


Input: However, it is often overlooked that the decoder only has a single view of the source sequences, that is, the representations generated by the last encoder layer, which is supposed to be a  global view of source sequences.


Input: However, it is often overlooked that the decoder only has a single view of the source sequences, that is, the representations generated by the last encoder layer, which is supposed to be a  global view of source sequences.


Input: However, it is often overlooked that the decoder only has a single view of the source sequences, that is, the representations generated by the last encoder layer, which is supposed to be a  global view of source sequences . Such implementation hinders the decoder from concrete, fine-grained, local source information.


Input: Such implementation hinders the decoder from concrete, fine-grained, local source information.


Input: In this work, we explore to reuse the representations from different encoder layers for layer-wise cross-view decoding, that is, different views of the source sequences are presented to different decoder layers.


Input: In this work, we explore to reuse the representations from different encoder layers for layer-wise cross-view decoding, that is, different views of the source sequences are presented to different decoder layers.


Input: In this work, we explore to reuse the representations from different encoder layers for layer-wise cross-view decoding, that is, different views of the source sequences are presented to different decoder layers . We investigate multiple, representative strategies for cross-view coding, of which the granularity consistent attention (GCA) strategy proves the most efficient and effective in the experiments on  neural machine translation task . Especially, GCA surpasses the previous state-of-the-art architecture on three machine translation datasets.


Input: We investigate multiple, representative strategies for cross-view coding, of which the granularity consistent attention (GCA) strategy proves the most efficient and effective in the experiments on  neural machine translation task . Especially, GCA surpasses the previous state-of-the-art architecture on three machine translation datasets.


Input: We investigate multiple, representative strategies for cross-view coding, of which the granularity consistent attention (GCA) strategy proves the most efficient and effective in the experiments on  neural machine translation task . Especially, GCA surpasses the previous state-of-the-art architecture on three machine translation datasets.


Input: We investigate multiple, representative strategies for cross-view coding, of which the granularity consistent attention (GCA) strategy proves the most efficient and effective in the experiments on  neural machine translation task . Especially, GCA surpasses the previous state-of-the-art architecture on three machine translation datasets.


Input: In this work, we propose to encourage the decoder to take the full advantage of the multi-level source representations for layer-wise cross-view decoding . Concretely, different views of the source sequences are presented to different decoder layers and multiple strategies are explored to route the source representations.


Input: In this work, we propose layer-wise cross-view decoding, where for each decoder layer, together with the representations from the last encoder layer, which serve as a global view, those from other encoder layers are supplemented for a stereoscopic view of the source sequences.


Input: Systematic experiments  show that we successfully address the hierarchy bypassing problem and substantially improve the performance of sequence-to-sequence learning with deep representations on diverse tasks .


Input: Deep leaning models have been used widely for various purposes in recent years in object recognition, self-driving cars, face recognition, speech recognition, sentiment analysis  and many others.


Input: Deep leaning models have been used widely for various purposes in recent years in object recognition, self-driving cars, face recognition, speech recognition, sentiment analysis  and many others.


Input: However, in recent years it has been shown that these models possess weakness to noises which forces the model to misclassify.


Input: This issue has been studied profoundly in  image and audio domain.


Input: Very little has been studied on this issue with respect to textual data.


Input: In this manuscript  we accumulated and analyzed different attacking techniques, various defense models on how to overcome this issue in order to provide a more comprehensive idea.


Input: In this manuscript  we accumulated and analyzed different attacking techniques, various defense models on how to overcome this issue in order to provide a more comprehensive idea.


Input: In this manuscript  we accumulated and analyzed different attacking techniques, various defense models on how to overcome this issue in order to provide a more comprehensive idea.


Input: Later we point out some of the interesting findings of all papers and challenges that need to be overcome in order to move forward in this field.


Input: Deep neural networks excel at learning from labeled data and achieve state-of-the-art results on a wide array of Natural Language Processing tasks.


Input: We outline methods, from early approaches in traditional non-neural methods to pre-trained model transfer.


Input: Lastly, we outline future directions, particularly the broader need for out-of-distribution generalization of future intelligent NLP.


Input: We motivate the development of our framework with several cautionary tales of previous re-search, which has developed numerous less-than-optimal algorithms for computing expectations and their gradients.


Input: In all cases, our approach matches the efficiency of existing algorithms and, in several cases, reducesthe runtime complexity by a factor (or two) of the sentence length.


Input: We find our algorithms are upto 12 and 26 times faster than previous algorithms for computing the Shannon entropy and the gradient of the generalized expectation objective, respectively.


Input: We give a general framework for inference in spanning tree models.


Input: We give a general framework for inference in spanning tree models. We propose unified algorithms for the important cases of first-order expectations and second-order expectations in edge-factored, non-projective spanning-tree models.


Input: We motivate the development of our framework with several cautionary tales of previous research, which has developed numerous less-than-optimal algorithms for computing expectations and their gradients.


Input: We demonstrate how our framework efficiently computes several quantities with known algorithms, including the  expected attachment score, entropy, and generalized expectation criteria.


Input: In all cases, our approach matches the efficiency of existing algorithms and, in several cases, reduces the runtime complexity by a factor (or two) of the sentence length.


Input: We validate the implementation of our framework through runtime experiments.


Input: We validate the implementation of our framework through runtime experiments. We find our algorithms are up to 12 and 26 times faster than previous algorithms for computing the Shannon entropy and the gradient of the generalized expectation objective, respectively.


Input: We propose a general framework for computing expectations in edge-factored, non-projective spanning-tree models.


Input: We demonstrate how our framework efficiently computes several quantities with known algorithms, including the Shannon entropy, the expected attachment score, and the generalized expectation criterion.


Input: We demonstrate how our framework efficiently computes several quantities with known algorithms, including the Shannon entropy, the expected attachment score, and the generalized expectation criterion.


Input: As a bonus, we give algorithms for quantities that are missing in the literature, including the gradient of entropy, the KL divergence, and the gradient of the KL divergence.


Input: We validate  our framework through rigorous proofs of correctness and efficiency.


Input: A reduction of quadratic time and memory complexity to sublinear was achieved due to a robust differentiable top-k operator.


Input: For example, our experiments on a challenging summarization task of long documents show that our method is much faster and up to 16 times more memory efficient while significantly outperforming both dense and state-of-the-art sparse transformer models.


Input: The method can be effortlessly applied to many models used in NLP and CV, simultaneously with other improvements since representation pooling addresses a different aspect of the attention's complexity problem.


Input: Inspired by Google's Neural Machine Translation (NMT) \mbox{%DIFAUXCMD Wu2016Google that models the one-to-one alignment in translation tasks with an optimal uniform attention distribution during the inference, this study proposes an attention-aware inference algorithm for Neural Abstractive Summarization (NAS) to regulate generated summaries to attend to source paragraphs/sentences with the optimal coverage.


Input: Unlike NMT, the attention-aware inference of NAS requires the prediction of the optimal attention distribution. Therefore, an attention-prediction model is constructed to learn the dependency between attention weights and sources.


Input: Therefore, an attention-prediction model is constructed to learn the dependency between attention weights and sources.


Input: Therefore, an attention-prediction model is constructed to learn the dependency between attention weights and sources. To apply the attention-aware inference on multi-document summarization, a Hierarchical Transformer (HT) is developed to accept lengthy inputs at the same time project cross-document information. Experiments on WikiSum \mbox{%DIFAUXCMD liu2018generating By refining the regular beam search with the attention-aware inference, significant improvements on the quality of summaries could be further observed.


Input: Inspired by Google's Neural Machine Translation (NMT) that models the one-to-one alignment in translation tasks with an uniform attention distribution during the inference, this study proposes an attention-aware inference algorithm for Neural Abstractive Summarization (NAS) to regulate generated summaries to attend to source contents with the optimal coverage.


Input: Inspired by Google's Neural Machine Translation (NMT) that models the one-to-one alignment in translation tasks with an uniform attention distribution during the inference, this study proposes an attention-aware inference algorithm for Neural Abstractive Summarization (NAS) to regulate generated summaries to attend to source contents with the optimal coverage. Unlike NMT, NAS is not based on one-to-one transformation. Instead, its attention distribution for the input should be irregular and depend on the content layout of the source documents. To address this matter, we construct an attention-prediction model to learn the dependency between the optimal attention distribution and the source . By refining the vanilla beam searchwith the attention-aware mechanism, significant improvements on the quality of summaries could be observed.


Input: Conventional sparse retrieval methods such as TF-IDF and BM25 are simple and efficient, but solely rely on lexical overlap without semantic matching. Recent dense retrieval methods learn latent representations to tackle the lexical mismatch problem, while being more computationally expensive and insufficient for exact matching as they embed the text sequence into a single vector with limited capacity. In this paper, we present Generation-Augmented Retrieval (GAR), a query expansion method that augments a query with relevant contexts through text generation .


Input: In this paper, we present Generation-Augmented Retrieval (GAR), a query expansion method that augments a query with relevant contexts through text generation .


Input: In this paper, we present Generation-Augmented Retrieval (GAR), a query expansion method that augments a query with relevant contexts through text generation .


Input: In this paper, we present Generation-Augmented Retrieval (GAR), a query expansion method that augments a query with relevant contexts through text generation .


Input: We demonstrate on open-domain question answering that the generated contexts significantly enrich the semantics of the queries and thus GAR with sparse representations (BM25) achieves comparable or better performance than the state-of-the-art dense  methods such as DPR \mbox{%DIFAUXCMD karpukhin2020dense.


Input: We demonstrate on open-domain question answering that the generated contexts significantly enrich the semantics of the queries and thus GAR with sparse representations (BM25) achieves comparable or better performance than the state-of-the-art dense  methods such as DPR \mbox{%DIFAUXCMD karpukhin2020dense.


Input: We demonstrate on open-domain question answering that the generated contexts significantly enrich the semantics of the queries and thus GAR with sparse representations (BM25) achieves comparable or better performance than the state-of-the-art dense  methods such as DPR \mbox{%DIFAUXCMD karpukhin2020dense.


Input: We demonstrate on open-domain question answering that the generated contexts significantly enrich the semantics of the queries and thus GAR with sparse representations (BM25) achieves comparable or better performance than the state-of-the-art dense  methods such as DPR \mbox{%DIFAUXCMD karpukhin2020dense.


Input: We demonstrate on open-domain question answering that the generated contexts significantly enrich the semantics of the queries and thus GAR with sparse representations (BM25) achieves comparable or better performance than the state-of-the-art dense  methods such as DPR \mbox{%DIFAUXCMD karpukhin2020dense.


Input: We show that generating various contexts of a query is beneficial as fusing their results consistently yields better retrieval accuracy.


Input: Furthermore, GAR achieves the state-of-the-art performance on the Natural Questions and TriviaQA datasets under the extractive setting when equipped with an extractive reader, and consistently outperforms other retrieval methods when the same generative reader is used.


Input: Furthermore, GAR achieves the state-of-the-art performance on the Natural Questions and TriviaQA datasets under the extractive setting when equipped with an extractive reader, and consistently outperforms other retrieval methods when the same generative reader is used.


Input: Furthermore, GAR achieves the state-of-the-art performance on the Natural Questions and TriviaQA datasets under the extractive setting when equipped with an extractive reader, and consistently outperforms other retrieval methods when the same generative reader is used.


Input: Hardware-agnostic programming with high performance portability will be the bedrock for realizing the ubiquitous adoption of emerging accelerator technologies in future heterogeneous high-performance computing (HPC) systems, which is the key to achieving the next level of HPC performance on an expanding accelerator landscape. In this paper, we present HALO 1.0, an open-ended extensible multi-agent software framework that implements a set of proposed hardware-agnostic accelerator orchestration (HALO) principles and a novel compute-centric message passing interface (C^2MPI) specification for enabling the portable and performance-optimized execution of hardware-agnostic application host codes across heterogeneous accelerator resources.


Input: In this paper, we present HALO 1.0, an open-ended extensible multi-agent software framework that implements a set of proposed hardware-agnostic accelerator orchestration (HALO) principles and a novel compute-centric message passing interface (C^2MPI) specification for enabling the portable and performance-optimized execution of hardware-agnostic application host codes across heterogeneous accelerator resources.


Input: In this paper, we present HALO 1.0, an open-ended extensible multi-agent software framework that implements a set of proposed hardware-agnostic accelerator orchestration (HALO) principles and a novel compute-centric message passing interface (C^2MPI) specification for enabling the portable and performance-optimized execution of hardware-agnostic application host codes across heterogeneous accelerator resources.


Input: In this paper, we present HALO 1.0, an open-ended extensible multi-agent software framework that implements a set of proposed hardware-agnostic accelerator orchestration (HALO) principles and a novel compute-centric message passing interface (C^2MPI) specification for enabling the portable and performance-optimized execution of hardware-agnostic application host codes across heterogeneous accelerator resources.


Input: The experiment results of evaluating eight widely used HPC subroutines based on Intel Xeon E5-2620 v4 CPUs,


Input: Intel Arria 10 GX FPGAs, and NVIDIA GeForce RTX 2080 Ti GPUs show that HALO 1.0 allows for a unified control flow for the host program to run across all the computing devices with a consistently maximum performance portability score of 1.0, which is 2x-861,883x higher than the OpenCL-based solution that suffers from an unstably low performance portability score. of the documentation of their work.


Input: Intel Arria 10 GX FPGAs, and NVIDIA GeForce RTX 2080 Ti GPUs show that HALO 1.0 allows for a unified control flow for the host program to run across all the computing devices with a consistently maximum performance portability score of 1.0, which is 2x-861,883x higher than the OpenCL-based solution that suffers from an unstably low performance portability score. of the documentation of their work.


Input: Intel Arria 10 GX FPGAs, and NVIDIA GeForce RTX 2080 Ti GPUs show that HALO 1.0 allows for a unified control flow for the host program to run across all the computing devices with a consistently maximum performance portability score of 1.0, which is 2x-861,883x higher than the OpenCL-based solution that suffers from an unstably low performance portability score. of the documentation of their work.


Input: Intel Arria 10 GX FPGAs, and NVIDIA GeForce RTX 2080 Ti GPUs show that HALO 1.0 allows for a unified control flow for the host program to run across all the computing devices with a consistently maximum performance portability score of 1.0, which is 2x-861,883x higher than the OpenCL-based solution that suffers from an unstably low performance portability score. of the documentation of their work.


Input: Intel Arria 10 GX FPGAs, and NVIDIA GeForce RTX 2080 Ti GPUs show that HALO 1.0 allows for a unified control flow for the host program to run across all the computing devices with a consistently maximum performance portability score of 1.0, which is 2x-861,883x higher than the OpenCL-based solution that suffers from an unstably low performance portability score. of the documentation of their work.


Input: Natural Language Processing (NLP) systems learn harmful societal biases that cause them to widely proliferate inequality as they are deployed in more and more situations.


Input: To address and combat this, the NLP community relies on a variety of metrics to identify and quantify bias in black-box modelsand to guide efforts at debiasing . Some of these metrics are intrinsic, and are measured in word embedding spaces, and some are extrinsic, which measure the bias present downstream in the tasks that the word embeddings are plugged into.


Input: To address and combat this, the NLP community relies on a variety of metrics to identify and quantify bias in black-box modelsand to guide efforts at debiasing . Some of these metrics are intrinsic, and are measured in word embedding spaces, and some are extrinsic, which measure the bias present downstream in the tasks that the word embeddings are plugged into.


Input: To address and combat this, the NLP community relies on a variety of metrics to identify and quantify bias in black-box modelsand to guide efforts at debiasing . Some of these metrics are intrinsic, and are measured in word embedding spaces, and some are extrinsic, which measure the bias present downstream in the tasks that the word embeddings are plugged into.


Input: To address and combat this, the NLP community relies on a variety of metrics to identify and quantify bias in black-box modelsand to guide efforts at debiasing . Some of these metrics are intrinsic, and are measured in word embedding spaces, and some are extrinsic, which measure the bias present downstream in the tasks that the word embeddings are plugged into.


Input: To address and combat this, the NLP community relies on a variety of metrics to identify and quantify bias in black-box modelsand to guide efforts at debiasing . Some of these metrics are intrinsic, and are measured in word embedding spaces, and some are extrinsic, which measure the bias present downstream in the tasks that the word embeddings are plugged into.


Input: To address and combat this, the NLP community relies on a variety of metrics to identify and quantify bias in black-box modelsand to guide efforts at debiasing . Some of these metrics are intrinsic, and are measured in word embedding spaces, and some are extrinsic, which measure the bias present downstream in the tasks that the word embeddings are plugged into. This research examines whether easy-to-measure intrinsic metrics correlate well to real world extrinsic metrics . We measure both intrinsic and extrinsic bias across hundreds of trained models covering different tasks and experimental conditions and find that there is no reliable correlation between these metrics that holds in all scenarios across tasks and languages.


Input: We measure both intrinsic and extrinsic bias across hundreds of trained models covering different tasks and experimental conditions and find that there is no reliable correlation between these metrics that holds in all scenarios across tasks and languages.


Input: We measure both intrinsic and extrinsic bias across hundreds of trained models covering different tasks and experimental conditions and find that there is no reliable correlation between these metrics that holds in all scenarios across tasks and languages.


Input: We advise that efforts to debias embedding spaces be always also paired with measurement of downstream model bias, and suggest that that community increase effort into making downstream measurement more feasible via creation of additional challenge sets and annotated test data.


Input: We advise that efforts to debias embedding spaces be always also paired with measurement of downstream model bias, and suggest that that community increase effort into making downstream measurement more feasible via creation of additional challenge sets and annotated test data.


Input: We advise that efforts to debias embedding spaces be always also paired with measurement of downstream model bias, and suggest that that community increase effort into making downstream measurement more feasible via creation of additional challenge sets and annotated test data.


Input: We additionally release code, a new intrinsic metric, and an annotated test set for gender bias for hatespeech.


Input: We additionally release code, a new intrinsic metric, and an annotated test set for gender bias for hatespeech.


Input: In this paper, we present CANINE, a neural encoder that operates directly on character sequences--without explicit tokenization or vocabulary--and a pre-training strategy with soft inductive biases in place of hard token boundaries.


Input: In this paper, we present CANINE, a neural encoder that operates directly on character sequences--without explicit tokenization or vocabulary--and a pre-training strategy with soft inductive biases in place of hard token boundaries.


Input: To use its finer-grained input effectively and efficiently, CANINE combines downsampling, which reduces the input sequence length, with a deep transformer stack, which encodes con-text.


Input: a binary classification (offensive versus non-offensive comments), the level of the offense (highly offensive, moderately offensive and slightly offensive messages), and the identification regarding the target of the discriminatory content (xenophobia, racism, homophobia, sexism, religion intolerance, partyism, apology to the dictatorship, antisemitism and fat phobia).


Input: a binary classification (offensive versus non-offensive comments), the level of the offense (highly offensive, moderately offensive and slightly offensive messages), and the identification regarding the target of the discriminatory content (xenophobia, racism, homophobia, sexism, religion intolerance, partyism, apology to the dictatorship, antisemitism and fat phobia).


Input: The proposed annotation process is also language and domain independent .


Input: This paperdescribes a corpus annotation process  to support the identification of hate speech and offensive language in social media.


Input: In addition, we provide the first robust corpus  this kind for the Brazilian Portuguese language.


Input: The corpus was collected from Instagram pages of political personalities and manually annotated, being composed by 7,000 documents annotated according to three different layers:


Input: a binary classification (offensive versus non-offensive language), the level of offense (highly offensive, moderately offensive  and slightly offensive messages), and the identification regarding the target of the discriminatory content (xenophobia, racism, homophobia, sexism, religion intolerance, partyism, apology to the dictatorship, antisemitism  and fat phobia).


Input: a binary classification (offensive versus non-offensive language), the level of offense (highly offensive, moderately offensive  and slightly offensive messages), and the identification regarding the target of the discriminatory content (xenophobia, racism, homophobia, sexism, religion intolerance, partyism, apology to the dictatorship, antisemitism  and fat phobia).


Input: a binary classification (offensive versus non-offensive language), the level of offense (highly offensive, moderately offensive  and slightly offensive messages), and the identification regarding the target of the discriminatory content (xenophobia, racism, homophobia, sexism, religion intolerance, partyism, apology to the dictatorship, antisemitism  and fat phobia).


Input: Each comment was annotated by three different annotators, which achieved high inter-annotator agreement.


Input: The  proposed annotation approach is also language and domain independent, nevertheless, it was currently applied for Brazilian Portuguese .


Input: The  proposed annotation approach is also language and domain independent, nevertheless, it was currently applied for Brazilian Portuguese .


Input: Our approach incorporates an offensive lexicon composed by implicit and explicit offensive and swearing expressions annotated with binary classes: context-dependent offensive and context-independent offensive.


Input: Our approach incorporates an offensive lexicon composed by implicit and explicit offensive and swearing expressions annotated with binary classes: context-dependent offensive and context-independent offensive.


Input: Due to the severity of the hate speech and offensive comments in Brazil  and the lack of research in Portuguese, Brazilian Portuguese is the language used to validate our method.


Input: Gordon Brown, who made a speech to the UK Labour Party todayThe UK Prime Minister, Gordon Brown, has made a speech to UK Labour Party members as part of his party's spring conference.


Input: Gordon Brown, who made a speech to the UK Labour Party todayThe UK Prime Minister, Gordon Brown, has made a speech to UK Labour Party members as part of his party's spring conference.


Input: On the subjects of employment and welfare, Mr. Brown said that his party "will offer new entitlements to the carers that serve this country [the UK] so well.


Input: When discussing crime in the speech, Mr. Brown claimed that his party has "already cut crime by 32\%."


Input: 2008 The 27th Young Designers' Exhibition, famed as the largest show of students' creations, recognized by International Council of Societies of Industrial Design (ICSID) since last year, started at Taipei World Trade Center today and will close at Sunday (May 18) with participations from 87 academical and industrial units in Taiwan and 20 units from United States, United Kingdom, Italy, Netherlands, New Zealand, and Australia to showcase varied achievements from design industry.


Input: 2008 The 27th Young Designers' Exhibition, famed as the largest show of students' creations, recognized by International Council of Societies of Industrial Design (ICSID) since last year, started at Taipei World Trade Center today and will close at Sunday (May 18) with participations from 87 academical and industrial units in Taiwan and 20 units from United States, United Kingdom, Italy, Netherlands, New Zealand, and Australia to showcase varied achievements from design industry.


Input: Besides for the early-announced "Wow!


Input: Taiwan Design Award", winners from "2008 Young Designers  Competition" and "2008 YODEX Interior Design Competition" will also be announced in this Saturday (May 17).


Input: 2008 The 27th Young Designers' Exhibition, famed as the largest show from students' creations, recognized by International Council of Societies of Industrial Design (ICSID) since last year, started at Taipei World Trade Center yesterday and will close at Sunday (May 18) with participations from 87 academical units in Taiwan and 20 units from United States, United Kingdom, Italy, Netherlands, New Zealand, and Australia to showcase varied achievements from designindustry.


Input: 2008 The 27th Young Designers' Exhibition, famed as the largest show from students' creations, recognized by International Council of Societies of Industrial Design (ICSID) since last year, started at Taipei World Trade Center yesterday and will close at Sunday (May 18) with participations from 87 academical units in Taiwan and 20 units from United States, United Kingdom, Italy, Netherlands, New Zealand, and Australia to showcase varied achievements from designindustry.


Input: 2008 The 27th Young Designers' Exhibition, famed as the largest show from students' creations, recognized by International Council of Societies of Industrial Design (ICSID) since last year, started at Taipei World Trade Center yesterday and will close at Sunday (May 18) with participations from 87 academical units in Taiwan and 20 units from United States, United Kingdom, Italy, Netherlands, New Zealand, and Australia to showcase varied achievements from designindustry.


Input: 2008 The 27th Young Designers' Exhibition, famed as the largest show from students' creations, recognized by International Council of Societies of Industrial Design (ICSID) since last year, started at Taipei World Trade Center yesterday and will close at Sunday (May 18) with participations from 87 academical units in Taiwan and 20 units from United States, United Kingdom, Italy, Netherlands, New Zealand, and Australia to showcase varied achievements from designindustry.


Input: 2008 The 27th Young Designers' Exhibition, opened on May 15 at the Taipei World Trade Center and will close at Sunday May 18.


Input: Not only several design competitions, sponsors like International Forum Design (iF), EPSON, MUJI (in Japanese: 無印良品, Mujirushi Ryōhin), Tsann Kuen Trans-nation Group will also showcase different solutions for  design, creative, and cultural industries.


Input: Not only several design competitions, sponsors like International Forum Design (iF), EPSON, MUJI (in Japanese: 無印良品, Mujirushi Ryōhin), Tsann Kuen Trans-nation Group will also showcase different solutions for  design, creative, and cultural industries.


Input: Not only several design competitions, sponsors like International Forum Design (iF), EPSON, MUJI (in Japanese: 無印良品, Mujirushi Ryōhin), Tsann Kuen Trans-nation Group will also showcase different solutions for  design, creative, and cultural industries.


Input: In addition, Taiwan Design Center, the show organizer, also designed several site events like "On-line Graduate Season Show"," Carrer Match-up","Creative and Cultural Showcase and Performance","Seminars of YODEX 2008" to link the actual exhibition with  on-line exhibition.


Input: In addition, Taiwan Design Center, the show organizer, also designed several site events like "On-line Graduate Season Show"," Carrer Match-up","Creative and Cultural Showcase and Performance","Seminars of YODEX 2008" to link the actual exhibition with  on-line exhibition.


Input: In addition, Taiwan Design Center, the show organizer, also designed several site events like "On-line Graduate Season Show"," Carrer Match-up","Creative and Cultural Showcase and Performance","Seminars of YODEX 2008" to link the actual exhibition with  on-line exhibition.


Input: In addition, Taiwan Design Center, the show organizer, also designed several site events like "On-line Graduate Season Show"," Carrer Match-up","Creative and Cultural Showcase and Performance","Seminars of YODEX 2008" to link the actual exhibition with  on-line exhibition.


Input: In addition, Taiwan Design Center, the show organizer, also designed several site events like "On-line Graduate Season Show"," Carrer Match-up","Creative and Cultural Showcase and Performance","Seminars of YODEX 2008" to link the actual exhibition with  on-line exhibition.


Input: Besides of the early-announced "Wow!


Input: One hundred students from Serbia who decide to start graduate studies abroad will get 15,000 a year scholarships from the Fund for young talents . Students who can apply need to have at least  8.5 GPA on 5 to 10 scale, and have to be younger then 26.


Input: One hundred students from Serbia who decide to start graduate studies abroad will get 15,000 a year scholarships from the Fund for young talents . Students who can apply need to have at least  8.5 GPA on 5 to 10 scale, and have to be younger then 26.


Input: One hundred students from Serbia who decide to start graduate studies abroad will get 15,000 a year scholarships from the Fund for young talents . Students who can apply need to have at least  8.5 GPA on 5 to 10 scale, and have to be younger then 26.


Input: One hundred students from Serbia who decide to start graduate studies abroad will get 15,000 a year scholarships from the Fund for young talents . Students who can apply need to have at least  8.5 GPA on 5 to 10 scale, and have to be younger then 26.


Input: One hundred students from Serbia who decide to start graduate studies abroad will get 15,000 a year scholarships from the Fund for young talents . Students who can apply need to have at least  8.5 GPA on 5 to 10 scale, and have to be younger then 26.


Input: Another condition is that they have to work in Serbia for at least 5 years after finishing their graduate studies.


Input: Winners will be decided based on their GPA and prestige of the university they apply to . Also, one thousand university seniors will be awarded 250 a month.


Input: Winners will be decided based on their GPA and prestige of the university they apply to . Also, one thousand university seniors will be awarded 250 a month.


Input: The Fund for young talents will cover about 2.5\% of students in Serbia.


Input: A video was released yesterday purporting to show the execution of 21 by supporters of (ISIL) .


Input: A video was released yesterday purporting to show the execution of 21 by supporters of (ISIL) .


Input: The video indicates that the Christians were targeted by ISIL because of their religion.


Input: The Coptic Orthodox Church stated they were "confident" justice would be carried out.


Input: The for by reports disputed by the Russian Consul  shot and killed an attacker on Thursday, after two individuals attempted to commit a robbery.


Input: The for by reports disputed by the Russian Consul  shot and killed an attacker on Thursday, after two individuals attempted to commit a robbery.


Input: As reported, lawyer Marcos Cesar Feres Braga so-named by the newspaper working for the, was driving with his family through the suburb of near the main complex when the incident occurred.


Input: Braga, trained and considered an expert according to reports in, took control of the attacker's firearm and proceeded to shoot dead the alleged attacker, while the second suspect fled the scene.


Input: Braga, trained and considered an expert according to reports in, took control of the attacker's firearm and proceeded to shoot dead the alleged attacker, while the second suspect fled the scene.


Input: "The vice consul got into a physical struggle with the assailant  and during the fight the aggressor's weapon fired shots.


Input: However, Russia's Consul General Vladimir Tokmakov has released a statement claiming no Russian diplomats or employees of the consulate general were involved in the incident.


Input: "Information circulating in the Brazilian press about the alleged shooting [...] of a Brazilian national by a Russian diplomat during an armed robbery does not reflect reality ".


Input: José Sócrates, prime minister of Portugal, said he was "satisfied with the decision of Volkswagen to produce a new model in the factory of Palmela," and considered that the decision,"reflected the confidence [of the investors] in the portuguese economy."


Input: By 2008  the factory at Palmela will be only producing the multi-purpose vehicle Sharan and the Eos models.


Input: By 2008  the factory at Palmela will be only producing the multi-purpose vehicle Sharan and the Eos models.


Input: With the end of the production of the multi-purpose vehicle, the factory needs to garantee new product lines, since the new Eos is insufficient to maintain the current 2,790 workers.


Input: David Parker today resigned from Cabinet;


Input: a day after he resigned his position as Attorney-General, after publicity around his filing of an incorrect declaration, with the Companies Office on behalf of Queens Park Mews Limited.


Input: a day after he resigned his position as Attorney-General, after publicity around his filing of an incorrect declaration, with the Companies Office on behalf of Queens Park Mews Limited.


Input: The declaration said that;


Input: the three shareholders had unanimously agreed not to appoint an auditor for the company, but according to Investigate Magazine, another shareholder  Russell Hyslop, had never been consulted about the matter.


Input: the three shareholders had unanimously agreed not to appoint an auditor for the company, but according to Investigate Magazine, another shareholder  Russell Hyslop, had never been consulted about the matter.


Input: Parker was saying early this morning, that he would persist to hold his other portfolios.


Input: Parker was saying early this morning, that he would persist to hold his other portfolios.


Input: Prime Minister Helen Clark released the news shortly before 11am (NZST) as she walked into a caucus meeting.


Input: She says "I have this morning accepted Mr Parker's resignation from all his portfolios." Knowingly authorising a false statement is an offence under the Companies Act.


Input: The maximum penalty punishable under section 373(4) for filing a false return is a fine of $200,000 or 5 years imprisonment.


Input: The maximum penalty punishable under section 373(4) for filing a false return is a fine of $200,000 or 5 years imprisonment.


Input: According to BBC NEWS, Merseyside police have apparently been called in and are working with the Crown Prosecution Service.


Input: An Earlier article in the Liverpool Echo, had however suggested that some employees of Liverpool City Council felt postings on the blog may be defamatory, a civil rather  criminal matter.


Input: An Earlier article in the Liverpool Echo, had however suggested that some employees of Liverpool City Council felt postings on the blog may be defamatory, a civil rather  criminal matter.


Input: Liverpool City Council, has so far declined to comment on the sites dissaperance, although the site had been previosuly blocked from the council's IT systems.


Input: Liverpool City Council, has so far declined to comment on the sites dissaperance, although the site had been previosuly blocked from the council's IT systems.


Input: Hewlett-Packard is in a scandal over the alegged illegal investigation of its own board members over leaks from the board to the press.


Input: Hewlett-Packard is in a scandal over the alegged illegal investigation of its own board members over leaks from the board to the press.


Input: The companys admits that it hired private investigators and that they used pretexting to get private phone records of it's own directors.


Input: Pretexting is where someone pretends to be the customer when calling the phone company to get their phone records.


Input: It is illegal in the State of California.


Input: VC investor Tom Perkins resigned from the board on May 22nd 2006 over the issue and requested that HP look into the methods used in it's leak inquiry.


Input: VC investor Tom Perkins resigned from the board on May 22nd 2006 over the issue and requested that HP look into the methods used in it's leak inquiry.


Input: VC investor Tom Perkins resigned from the board on May 22nd 2006 over the issue and requested that HP look into the methods used in it's leak inquiry.


Input: The FCC is looking into phone companies who may have supplied the phone records illegally under California law. So


Input: The FCC is looking into phone companies who may have supplied the phone records illegally under California law. So


Input: 73-year old retired pastor, Reverend Roland Weisselberg, burned himself alive during Reformation Day services on Tuesday in Erfurt, Germany.


Input: His self-immolation was apparently to protest against the spread of Islam, which he felt the Protestant church should take more seriously.


Input: Reverend Weisselberg had lived under Communism in East Germany, and had been a publisher in his former vocation.


Input: Nine people are confirmed dead in a fire in a leather bag facory in Kolkata's South 24 Pargana district in Topsia area.


Input: Eighteen people were seriously injured and admitted to  National Medical College and Hospital.


Input: Inspector General of Police (Law and Order) Raj Kanojia said that the fire was broken out at about 3 am when the victims were inside the factory and all the doors were closed.


Input: Inspector General of Police (Law and Order) Raj Kanojia said that the fire was broken out at about 3 am when the victims were inside the factory and all the doors were closed.


Input: At the time of fire, all the premises were closed and workers could not come out from the factory.


Input: The cause of the fire was yet to be ascertained.


Input: Kolkata Mayor Bikash Ranjan Bhattacharya predicted short circuit or an unattended cigarette causing fire in the factory.


Input: Kolkata Mayor Bikash Ranjan Bhattacharya predicted short circuit or an unattended cigarette causing fire in the factory.


Input: It is also "any of the large bodies of water into which the great ocean is divided".


Input: A common definition lists five oceans, in descending order by area, the Pacific, Atlantic, Indian, Southern (Antarctic), and Arctic Oceans.


Input: A common definition lists five oceans, in descending order by area, the Pacific, Atlantic, Indian, Southern (Antarctic), and Arctic Oceans.


Input: The ocean is divided into five oceans: Pacific (the largest), Atlantic, Indian, Southern (Antarctic), and Arctic (the smallest).


Input: The ocean is divided into five oceans: Pacific (the largest), Atlantic, Indian, Southern (Antarctic), and Arctic (the smallest).


Input: As the world's ocean is the principal component of Earth's hydrosphere, it is integral to life on Earth, forms part of the carbon cycle and water cycle, and - as a huge heat reservoir - influences climate and weather patterns.


Input: As the world's ocean is the principal component of Earth's hydrosphere, it is integral to life on Earth, forms part of the carbon cycle and water cycle, and - as a huge heat reservoir - influences climate and weather patterns.


Input: As the world's ocean is the principal component of Earth's hydrosphere, it is integral to life on Earth, forms part of the carbon cycle and water cycle, and - as a huge heat reservoir - influences climate and weather patterns.


Input: As the world's ocean is the principal component of Earth's hydrosphere, it is integral to life on Earth, forms part of the carbon cycle and water cycle, and - as a huge heat reservoir - influences climate and weather patterns.


Input: Oceanographers divide the ocean into different vertical and horizontal zones defined by physical and biological conditions.


Input: The pelagic zone consists of the water column of the open ocean, and can be divided into further regions categorized by light abundance and by depth.


Input: The pelagic zone consists of the water column of the open ocean, and can be divided into further regions categorized by light abundance and by depth.


Input: The pelagic zone consists of the water column of the open ocean, and can be divided into further regions categorized by light abundance and by depth.


Input: The photic zone includes the oceans from the surface to a depth of 200m;


Input: The photic zone includes the oceans from the surface to a depth of 200m; it is the region where photosynthesis can occur and is, therefore, the most biodiverse.


Input: it is the region where photosynthesis can occur and is, therefore, the most biodiverse.


Input: It is this upper sunlit ocean that creates the food supply that ultimately sustains most of the ocean ecosystem  . Light only penetrates to a  few hundred meters depth, so the remaining ocean below this is cold and dark.


Input: It is this upper sunlit ocean that creates the food supply that ultimately sustains most of the ocean ecosystem  . Light only penetrates to a  few hundred meters depth, so the remaining ocean below this is cold and dark.


Input: It is this upper sunlit ocean that creates the food supply that ultimately sustains most of the ocean ecosystem  . Light only penetrates to a  few hundred meters depth, so the remaining ocean below this is cold and dark.


Input: It is this upper sunlit ocean that creates the food supply that ultimately sustains most of the ocean ecosystem  . Light only penetrates to a  few hundred meters depth, so the remaining ocean below this is cold and dark.


Input: The continental shelf around the edge of the oceans is shallower (a few hundred meters or less) and this region is most impacted by human activity.


Input: During study of fluid mechanics, researchers attempted to give a correct vision of maritime currents. The origin of currents is owed to physical differences between various water masses, the main parameter being the difference of density that varies in function of the temperature and the concentration of salt.


Input: The origin of currents is owed to physical differences between various water masses, the main parameter being the difference of density that varies in function of the temperature and the concentration of salt.


Input: The study of these currents, combined with other factors such as tides (producing a change in sea level) and wind (originally swell) for understanding the marine hydrodynamics and different processes which are linked there as sediment movement and the climate balance.


Input: The study of these currents, combined with other factors such as tides (producing a change in sea level) and wind (originally swell) for understanding the marine hydrodynamics and different processes which are linked there as sediment movement and the climate balance.


Input: The study of these currents, combined with other factors such as tides (producing a change in sea level) and wind (originally swell) for understanding the marine hydrodynamics and different processes which are linked there as sediment movement and the climate balance.


Input: A trainee is commonly known as an individual taking part in a trainee program or a graduate program within a company after having graduated from university or college.


Input: A trainee is commonly known as an individual taking part in a trainee program or a graduate program within a company after having graduated from university or college.


Input: Trainee programs and graduate programs are arranged by private companies and public sector employers where the trainee is offered the possibility to take part 6 to 20 months training programs . During the duration of these programs, the trainee is expected to receive a salary as well as is expected to have full-time employment awaiting in the company when the program is over.


Input: Trainee programs and graduate programs are arranged by private companies and public sector employers where the trainee is offered the possibility to take part 6 to 20 months training programs . During the duration of these programs, the trainee is expected to receive a salary as well as is expected to have full-time employment awaiting in the company when the program is over.


Input: Often used as an insurance measure by companies, firms typically will have a trainee period (23 months) where the person is still being evaluated after which an official decision to hire on a permanent basis is made.


Input: Thenumgal Poulose Jacob is an Indian surgeon specialized in vascular surgery, and the founder Head of the Department of Vascular Surgery at Madras Medical College.


Input: Born in Aluva, in the south Indian state of Kerala  to Thenumgal Poulose and Mariam in a Malayali family, he did his under-graduate studies at UC College before graduating in medicine from Stanley Medical College, Chennai  and secured his MS degree from the same institution.


Input: Born in Aluva, in the south Indian state of Kerala  to Thenumgal Poulose and Mariam in a Malayali family, he did his under-graduate studies at UC College before graduating in medicine from Stanley Medical College, Chennai  and secured his MS degree from the same institution.


Input: He started his practice under government service at Madras Medical College where he helped establish the department of vascular surgery in 1978 and served as its founder head till his superannuation in 1993.


Input: He is doing private practice at T. P. Jacob Clinic in Royapuram, Chennai and is also a consultant vascular surgeon at MV Hospitals for Diabetes, Royapuram.


Input: is a model and endorser in Philippines,


Input: He is known as one of the winners in Mister Philippines 2011,


Input: which is made to the audience of his appeal, he is look a like an actor in the Philippines, JM de URL Biography Lopez was born and raised in the southern tip of Metro Manila, before he joined the pageant of Mister Philippines, Fhrancis work as a model and he signing contracted in a Go Green Artists his agency and the Project Management, After he won and got the prize, Fhrancis held pageant of Patravadi Theatre, Lopez is one of the finalists Top 16  in Mister International 2011.


Input: which is made to the audience of his appeal, he is look a like an actor in the Philippines, JM de URL Biography Lopez was born and raised in the southern tip of Metro Manila, before he joined the pageant of Mister Philippines, Fhrancis work as a model and he signing contracted in a Go Green Artists his agency and the Project Management, After he won and got the prize, Fhrancis held pageant of Patravadi Theatre, Lopez is one of the finalists Top 16  in Mister International 2011.


Input: which is made to the audience of his appeal, he is look a like an actor in the Philippines, JM de URL Biography Lopez was born and raised in the southern tip of Metro Manila, before he joined the pageant of Mister Philippines, Fhrancis work as a model and he signing contracted in a Go Green Artists his agency and the Project Management, After he won and got the prize, Fhrancis held pageant of Patravadi Theatre, Lopez is one of the finalists Top 16  in Mister International 2011.


Input: which is made to the audience of his appeal, he is look a like an actor in the Philippines, JM de URL Biography Lopez was born and raised in the southern tip of Metro Manila, before he joined the pageant of Mister Philippines, Fhrancis work as a model and he signing contracted in a Go Green Artists his agency and the Project Management, After he won and got the prize, Fhrancis held pageant of Patravadi Theatre, Lopez is one of the finalists Top 16  in Mister International 2011.


Input: which is made to the audience of his appeal, he is look a like an actor in the Philippines, JM de URL Biography Lopez was born and raised in the southern tip of Metro Manila, before he joined the pageant of Mister Philippines, Fhrancis work as a model and he signing contracted in a Go Green Artists his agency and the Project Management, After he won and got the prize, Fhrancis held pageant of Patravadi Theatre, Lopez is one of the finalists Top 16  in Mister International 2011.


Input: which is made to the audience of his appeal, he is look a like an actor in the Philippines, JM de URL Biography Lopez was born and raised in the southern tip of Metro Manila, before he joined the pageant of Mister Philippines, Fhrancis work as a model and he signing contracted in a Go Green Artists his agency and the Project Management, After he won and got the prize, Fhrancis held pageant of Patravadi Theatre, Lopez is one of the finalists Top 16  in Mister International 2011.


Input: which is made to the audience of his appeal, he is look a like an actor in the Philippines, JM de URL Biography Lopez was born and raised in the southern tip of Metro Manila, before he joined the pageant of Mister Philippines, Fhrancis work as a model and he signing contracted in a Go Green Artists his agency and the Project Management, After he won and got the prize, Fhrancis held pageant of Patravadi Theatre, Lopez is one of the finalists Top 16  in Mister International 2011.


Input: which is made to the audience of his appeal, he is look a like an actor in the Philippines, JM de URL Biography Lopez was born and raised in the southern tip of Metro Manila, before he joined the pageant of Mister Philippines, Fhrancis work as a model and he signing contracted in a Go Green Artists his agency and the Project Management, After he won and got the prize, Fhrancis held pageant of Patravadi Theatre, Lopez is one of the finalists Top 16  in Mister International 2011.


Input: which is made to the audience of his appeal, he is look a like an actor in the Philippines, JM de URL Biography Lopez was born and raised in the southern tip of Metro Manila, before he joined the pageant of Mister Philippines, Fhrancis work as a model and he signing contracted in a Go Green Artists his agency and the Project Management, After he won and got the prize, Fhrancis held pageant of Patravadi Theatre, Lopez is one of the finalists Top 16  in Mister International 2011.


Input: Mister Philippines 2011 & Mister International 2011 Lopez  declared as one of the winner of Mister Philippines 2011, After he won the competition on December 17, 2011, Lopez joined the compete in the Mister International 2011 pageant which is happening in Pattaya, Thailand and Bangkok.


Input: Mister Philippines 2011 & Mister International 2011 Lopez  declared as one of the winner of Mister Philippines 2011, After he won the competition on December 17, 2011, Lopez joined the compete in the Mister International 2011 pageant which is happening in Pattaya, Thailand and Bangkok.


Input: Mister Philippines 2011 & Mister International 2011 Lopez  declared as one of the winner of Mister Philippines 2011, After he won the competition on December 17, 2011, Lopez joined the compete in the Mister International 2011 pageant which is happening in Pattaya, Thailand and Bangkok.


Input: Mister Philippines 2011 & Mister International 2011 Lopez  declared as one of the winner of Mister Philippines 2011, After he won the competition on December 17, 2011, Lopez joined the compete in the Mister International 2011 pageant which is happening in Pattaya, Thailand and Bangkok.


Input: Mister Philippines 2011 & Mister International 2011 Lopez  declared as one of the winner of Mister Philippines 2011, After he won the competition on December 17, 2011, Lopez joined the compete in the Mister International 2011 pageant which is happening in Pattaya, Thailand and Bangkok.


Input: Paderap has four sons, namely Tuanku Jalaluddin (or Kejuruan Metar), Tuanku Panglima Pasutan (or Kejuruan Padang), Tuanku Tawar (or Kejuruan Santun), and Tuanku Umar (or Kejuruan Junjongan).


Input: Jalaluddin, Paderap's first son, cannot replace him because of a physical disability.


Input: In the end, it was Pasutan who became the fourth ruler of Deli, while his younger brother Umar got a separate territory and became the first ruler of the Serdang Sultanate.


Input: In the end, it was Pasutan who became the fourth ruler of Deli, while his younger brother Umar got a separate territory and became the first ruler of the Serdang Sultanate.


Input: Tagging, or using gates, has been used to enable translation models to distinguish between synthetic and natural data. This improves standard back-translation and also enables the use of iterative back-translation on language pairs that underperformed using standard back-translation.


Input: This improves standard back-translation and also enables the use of iterative back-translation on language pairs that underperformed using standard back-translation.


Input: This improves standard back-translation and also enables the use of iterative back-translation on language pairs that underperformed using standard back-translation.


Input: This work presents a simplified approach of differentiating between the two data using pretraining and finetuning.


Input: This work presents a simplified approach of differentiating between the two data using pretraining and finetuning.


Input: The approach - tag-less back-translation - trains the model on the synthetic data and finetunes it on the natural data.


Input: The approach - tag-less back-translation - trains the model on the synthetic data and finetunes it on the natural data. Preliminary experiments have shown the approach to continuously outperform the tagging approach on low resource English-Vietnamese neural machine translation.


Input: Preliminary experiments have shown the approach to continuously outperform the tagging approach on low resource English-Vietnamese neural machine translation.


Input: While the need for tagging (noising) the dataset has been removed, the approach outperformed the tagged back-translation approach by an average of 0.4 BLEU .


Input: While the need for tagging (noising) the dataset has been removed, the approach outperformed the tagged back-translation approach by an average of 0.4 BLEU .


Input: With the recent success of  pre-training technique for NLP and image-linguistic tasks, there are still few works on video-linguistic pre-training.


Input: With the recent success of  pre-training technique for NLP and image-linguistic tasks, there are still few works on video-linguistic pre-training.


Input: Besides, most of the existing multimodal models are pre-trained for understanding task, which leads to a pretrain-finetune discrepency for generation tasks.


Input: Besides, most of the existing multimodal models are pre-trained for understanding task, which leads to a pretrain-finetune discrepency for generation tasks.


Input: In this paper, we propose UniViLM: a Unified Video and Language pre-training Model for both multimodal understanding and generation.


Input: In this paper, we propose UniViLM: a Unified Video and Language pre-training Model for both multimodal understanding and generation.


Input: Our model comprises of 4 components including two single-modal encoders, a cross encoder  and a decoder with the Transformer backbone.


Input: Our model comprises of 4 components including two single-modal encoders, a cross encoder  and a decoder with the Transformer backbone.


Input: We first pre-train our model to learn the universal representation for both video and language on a large instructional video dataset. Then we fine-tune the model on two multimodal tasks including understanding task (text-based video retrieval) and generation task (multimodal video captioning). Our extensive experiments show that our method can improve the performance of both understanding and generation tasks and achieves the state-of-the art results.


Input: Following each patient visit, physicians must draft detailed clinical summaries called SOAP notes.


Input: For all the benefits of this documentation the process remains onerous, contributing to increasing physician burnout.


Input: For all the benefits of this documentation the process remains onerous, contributing to increasing physician burnout.


Input: In a parallel development, patients increasingly record audio from their visits (with consent), often through dedicated apps. In this paper, we present the first study to evaluate complete pipelines for leveraging these transcripts to train machine learning model to  generate these notes.


Input: In this paper, we present the first study to evaluate complete pipelines for leveraging these transcripts to train machine learning model to  generate these notes.


Input: In this paper, we present the first study to evaluate complete pipelines for leveraging these transcripts to train machine learning model to  generate these notes . We first describe a unique dataset of patient visit records, consisting of transcripts, paired SOAP notes, and annotations marking noteworthy utterances that support each summary sentence.


Input: We first describe a unique dataset of patient visit records, consisting of transcripts, paired SOAP notes, and annotations marking noteworthy utterances that support each summary sentence.


Input: Our best performing method first (i) extracts noteworthy utterances via multi-label classification assigns them to summary section(s);


Input: Compared to an end-to-end approach that generates the full SOAP note from the full conversation, our approach improves by 7 ROUGE-1 points . Oracle experiments indicate that fixing our generative capabilities, improvements in extraction alone could provide (up to) a further 9 ROUGE point gain.


Input: This paper presents a new method SOLOIST, which uses transfer learning to efficiently build task-oriented dialog systems at scale.


Input: This paper presents a new method SOLOIST, which uses transfer learning to efficiently build task-oriented dialog systems at scale.


Input: This paper presents a new method SOLOIST, which uses transfer learning to efficiently build task-oriented dialog systems at scale.


Input: We parameterize a dialog system using a Transformer-based auto-regressive language model, which subsumes different dialog modules (e.g., state tracker, dialog policy, response generator) into a single neural model.


Input: We parameterize a dialog system using a Transformer-based auto-regressive language model, which subsumes different dialog modules (e.g., state tracker, dialog policy, response generator) into a single neural model.


Input: We pre-train, on large heterogeneous dialog corpora, a large-scale Transformer model which can generate dialog responses grounded in user goals and real-world knowledge for task completion.


Input: We pre-train, on large heterogeneous dialog corpora, a large-scale Transformer model which can generate dialog responses grounded in user goals and real-world knowledge for task completion.


Input: The pre-trained model can be efficiently adapted to accomplish a new dialog task with a handful of task-specific dialogs via machine teaching.


Input: Our experiments demonstrate that (i) SOLOIST creates new state-of-the-art results on two well-known benchmarks, CamRest and MultiWOZ, (ii) in the few-shot learning setting, the dialog systems developed by SOLOIST significantly outperform those developed by existing methods, and (iii) the use of machine teaching substantially reduces the labeling cost.


Input: Our experiments demonstrate that (i) SOLOIST creates new state-of-the-art results on two well-known benchmarks, CamRest and MultiWOZ, (ii) in the few-shot learning setting, the dialog systems developed by SOLOIST significantly outperform those developed by existing methods, and (iii) the use of machine teaching substantially reduces the labeling cost.


Input: Our experiments demonstrate that (i) SOLOIST creates new state-of-the-art results on two well-known benchmarks, CamRest and MultiWOZ, (ii) in the few-shot learning setting, the dialog systems developed by SOLOIST significantly outperform those developed by existing methods, and (iii) the use of machine teaching substantially reduces the labeling cost . We will release our code and pre-trained models for reproducible research.


Input: Stance detection on social media is an emerging opinion mining paradigm for various social and political applications where sentiment analysis might be sub-optimal.


Input: An exhaustive review of stance detection techniques on social media is presented, including the task definition, the different types of targets in stance detection, the features set used, and the various machine learning approaches applied.


Input: An exhaustive review of stance detection techniques on social media is presented, including the task definition, the different types of targets in stance detection, the features set used, and the various machine learning approaches applied.


Input: An exhaustive review of stance detection techniques on social media is presented, including the task definition, the different types of targets in stance detection, the features set used, and the various machine learning approaches applied.


Input: An exhaustive review of stance detection techniques on social media is presented, including the task definition, the different types of targets in stance detection, the features set used, and the various machine learning approaches applied.


Input: An exhaustive review of stance detection techniques on social media is presented, including the task definition, the different types of targets in stance detection, the features set used, and the various machine learning approaches applied.


Input: The survey reports the state-of-the-art results on the existing benchmark datasets on stance detection, and discusses the most effective approaches.


Input: In addition, this study explores the emerging trends and the different applications of stance detection on social media.


Input: The study concludes by providing discussion of the gaps in the current existing research and highlighting the possible future directions for stance detection on social media.


Input: The study concludes by providing discussion of the gaps in the current existing research and highlighting the possible future directions for stance detection on social media.


Input: Recently, neural-symbolic models have achieved noteworthy success in leveraging knowledge graphs (KGs) for commonsense reasoning tasks, like question answering (QA). However, fact sparsity, inherent in human-annotated KGs, can hinder such models from retrieving task-relevant knowledge.


Input: Recently, neural-symbolic models have achieved noteworthy success in leveraging knowledge graphs (KGs) for commonsense reasoning tasks, like question answering (QA). However, fact sparsity, inherent in human-annotated KGs, can hinder such models from retrieving task-relevant knowledge.


Input: Given a KG subgraphof extracted facts, HGN is jointly trained to generate complementary facts, encode relational information in the resulting "hybrid" subgraph, and filter out task-irrelevant facts.


Input: Given a KG subgraphof extracted facts, HGN is jointly trained to generate complementary facts, encode relational information in the resulting "hybrid" subgraph, and filter out task-irrelevant facts.


Input: Given a KG subgraphof extracted facts, HGN is jointly trained to generate complementary facts, encode relational information in the resulting "hybrid" subgraph, and filter out task-irrelevant facts.


Input: We demonstrate HGN's ability to produce contextually pertinent subgraphs by showing considerable performance gains across four commonsense reasoning benchmarks and a user study of fact validness and helpfulness.


Input: We demonstrate HGN's ability to produce contextually pertinent subgraphs by showing considerable performance gains across four commonsense reasoning benchmarks and a user study of fact validness and helpfulness.


Input: Driven by the increasingly larger deep learning models, neural language generation (NLG) has enjoyed unprecedentedly improvement and is now able to generate a diversity of human-like texts on demand, granting itself the capability of serving as a human writing assistant. Text attribute transfer is one of the most important NLG tasks, which aims to control certain attributes that people may expect the texts to possess, such as sentiment, tense, emotion, political position, etc.


Input: Text attribute transfer is one of the most important NLG tasks, which aims to control certain attributes that people may expect the texts to possess, such as sentiment, tense, emotion, political position, etc.


Input: It has a long history in Natural Language Processing but recently gains much more attention thanks to the promising performance brought by deep learning models.


Input: In this article, we present a systematic survey on these works for neural text attribute transfer.


Input: In this article, we present a systematic survey on these works for neural text attribute transfer.


Input: However, we still don't know much about the mechanism inside these internal representationsproduced by BERT . Do they have any common patterns?


Input: However, we still don't know much about the mechanism inside these internal representationsproduced by BERT . Do they have any common patterns?


Input: What are the relations between word sense and context?


Input: We find that nearly all the contextualized word vectors of BERT and RoBERTa have some common patterns.


Input: For RoBERTa, the 588^{th} element is always the largest  and the 77^{th} element is the smallest.


Input: We call them as "tails" of models.


Input: We  find that these "tails" are the major cause of anisotrpy of the vector space.


Input: After "cutting the tails", the same word's different vectors are more similar to each other.


Input: The internal representations also perform better on word-in-context (WiC) task.


Input: However, their memory footprint, inference latency, and power consumption are prohibitive for many edgeprocessors, and it has been a challenge to deploy these models for edge applications and devices that have resource constraints.


Input: While quantization can be a viable solution to this, previous work on quantizing Transformer based models uses floating-point arithmetic during inference, thus limiting model deployment on many edge processors.


Input: While quantization can be a viable solution to this, previous work on quantizing Transformer based models uses floating-point arithmetic during inference, thus limiting model deployment on many edge processors.


Input: In this work, we propose a novel integer-only quantization scheme for Transformer based models that quantizes the entire inference process.


Input: In particular, we demonstrate how to approximate nonlinear operationsin Transformer architectures, e.g., GELU, Softmax, and Layer Normalization, with lightweight integer computations. We use those approximations in our method, I-BERT, with an end-to-end integer-only inference, and without any floating point calculation.


Input: We use those approximations in our method, I-BERT, with an end-to-end integer-only inference, and without any floating point calculation.


Input: We use those approximations in our method, I-BERT, with an end-to-end integer-only inference, and without any floating point calculation.


Input: We test our approach on GLUE downstream tasks using RoBERTa-Base and RoBERTa-Large.


Input: We test our approach on GLUE downstream tasks using RoBERTa-Base and RoBERTa-Large. For both cases, with an 8-bit integer-only quantization scheme, I-BERT achieves similar  accuracy as compared to the full-precision baseline .


Input: For both cases, with an 8-bit integer-only quantization scheme, I-BERT achieves similar  accuracy as compared to the full-precision baseline .


Input: Based on this shortcut measurement, we propose a shortcut mitigation framework LGTR, to suppress the model from making overconfident predictions for samples with large shortcut degree.


Input: Experimental analysis further indicates that LGTR can improve the generalization accuracy on OOD data, while preserving the accuracy on in-distribution data.


Input: A car crash in the British region of Leicestershire has resulted in the deaths of six people, three men and three women.


Input: The incident  occurred at around 01:00 UTC today (0200 local time) and it involved a lorry and a Ford Mondeo on the A607 road.


Input: The incident  occurred at around 01:00 UTC today (0200 local time) and it involved a lorry and a Ford Mondeo on the A607 road.


Input: Location of Leicestershire within England Simon Adkin, from the local police, said that " The black Mondeo was travelling towards Leicester and the lorry was travelling in the opposite direction."


Input: Location of Leicestershire within England Simon Adkin, from the local police, said that " The black Mondeo was travelling towards Leicester and the lorry was travelling in the opposite direction."


Input: Location of Leicestershire within England Simon Adkin, from the local police, said that " The black Mondeo was travelling towards Leicester and the lorry was travelling in the opposite direction."


Input: He then said that the police "are appealing to anyone who witnessed the incident and saw either of these vehicles beforehand to get in touch."


Input: The driver of the Ford, who was killed in the incident, was a 23-year-old male. In addition to the 23-year-old male, an 18-year-old man, a 19-year old woman and an 18-year-old woman were also killed in the incident.


Input: In addition to the 23-year-old male, an 18-year-old man, a 19-year old woman and an 18-year-old woman were also killed in the incident.


Input: The age of the other two people is not yet known.


Input: The age of the other two people is not yet known.


Input: Fans have been queing since Thursday morning when a group of fans from Holland, Italy and Canada started queing outside the stadium.


Input: Croke Park is the fourth largest stadium in Europe, with an official capacity of 82,000 - bigger than both Cardiff's Millennium Stadium and Paris' Stade de France  . Croke Park is used to host GAA matches in sports such as hurling and Gaelic football and regularly attracts audiences of 60,000 or more in the summer months.


Input: Croke Park is the fourth largest stadium in Europe, with an official capacity of 82,000 - bigger than both Cardiff's Millennium Stadium and Paris' Stade de France  . Croke Park is used to host GAA matches in sports such as hurling and Gaelic football and regularly attracts audiences of 60,000 or more in the summer months.


Input: Sports presenter for 3 News has given an unsatisfactory letter of apology after he abused a taxi driver in Taupo while he was drunk.


Input: Sports presenter for 3 News has given an unsatisfactory letter of apology after he abused a taxi driver in Taupo while he was drunk.


Input: Brown sent an unsigned letter of apology to Wall, which Wall said she received last week, she had asked for one after the incident occurred.


Input: Wall said she was unhappy with the letter  and has written to TV3 to complain.


Input: Wall said that the letter was two paragraphs long and said that due to a concussion Brown cannot remember the incident  but if he did do something wrong then he is sorry.


Input: The letter contained no return address but she said:"I have written to TV3 [about the letter]."


Input: However Wall has admitted that she did make some comments about Brown after the incident, Wall said:"But it has gone on and on." Brown was stood down after the incident by TV3 and Roger Beaumont, a spokesman for TV3, said that he is still not back at work and it is unsure when,  if, he returns ."The investigation is still continuing.


Input: However Wall has admitted that she did make some comments about Brown after the incident, Wall said:"But it has gone on and on." Brown was stood down after the incident by TV3 and Roger Beaumont, a spokesman for TV3, said that he is still not back at work and it is unsure when,  if, he returns ."The investigation is still continuing.


Input: However Wall has admitted that she did make some comments about Brown after the incident, Wall said:"But it has gone on and on." Brown was stood down after the incident by TV3 and Roger Beaumont, a spokesman for TV3, said that he is still not back at work and it is unsure when,  if, he returns ."The investigation is still continuing.


Input: However Wall has admitted that she did make some comments about Brown after the incident, Wall said:"But it has gone on and on." Brown was stood down after the incident by TV3 and Roger Beaumont, a spokesman for TV3, said that he is still not back at work and it is unsure when,  if, he returns ."The investigation is still continuing.


Input: TV3 was also conducting their own investigation of which included hiring a private investigator.


Input: They said that they are not ruling out any disciplinary action if the allegations are proved to be correct.


Input: Today at a special caucus meeting, member of parliaments (MP) for National agreed that the new leader of the opposition, New Zealand's National Party, is John Key and the deputy leader is Bill English, they are taking over from past leader, Dr Don Brash and past deputy leader, Gerry Brownlee.


Input: Bill English, 45-years-old, use to be leader of the National Party before being outed in 2003 and being replaced by Dr Brash for a bad performance at the 2002 New Zealand elections,  Mr English only managed to get 22\% of the votes cast.


Input: Bill English, 45-years-old, use to be leader of the National Party before being outed in 2003 and being replaced by Dr Brash for a bad performance at the 2002 New Zealand elections,  Mr English only managed to get 22\% of the votes cast.


Input: Bill English, 45-years-old, use to be leader of the National Party before being outed in 2003 and being replaced by Dr Brash for a bad performance at the 2002 New Zealand elections,  Mr English only managed to get 22\% of the votes cast.


Input: Mr Key, also 45-years-old, said at the announcement of the new leader and deputy leader that he was honoured that he was chosen to be the new leader of the National Party and also hounoured that Mr English would be his  deputy.


Input: Mr Key, also 45-years-old, said at the announcement of the new leader and deputy leader that he was honoured that he was chosen to be the new leader of the National Party and also hounoured that Mr English would be his  deputy.


Input: "Can I tell you I think we will make a formidable team  . Key said the public placed a high value on unity.


Input: He is at number 3 on the list of National MP, behind Key and English.


Input: His new caucus responsibilities will be: Spokesman for the Security and Intelligence Service and Spokesman for Relationships with Non-Government Parties.


Input: A cannabis leaf The Australian state of New South Wales (NSW) launched its latest anti-cannabis propaganda campaign in Sydney today.


Input: The propaganda campaign, which specifically targets 14 to 19 year-olds aims to reduce the number of young people experimenting with the soft drug.


Input: The propaganda campaign, which specifically targets 14 to 19 year-olds aims to reduce the number of young people experimenting with the soft drug.


Input: The advertisements are the result of a tightening of cannabis laws in NSW last year.


Input: The propaganda, which NSW Health has cost at AUD$600,000, that could of been spent on feeding starving children, will use a variety of print ads placed at bus stops and in youth magazines in addition to propaganda messages on websites such as MySpace and MSN. Propaganda ads use a tag line saying "Pot.


Input: The propaganda, which NSW Health has cost at AUD$600,000, that could of been spent on feeding starving children, will use a variety of print ads placed at bus stops and in youth magazines in addition to propaganda messages on websites such as MySpace and MSN. Propaganda ads use a tag line saying "Pot.


Input: The propaganda, which NSW Health has cost at AUD$600,000, that could of been spent on feeding starving children, will use a variety of print ads placed at bus stops and in youth magazines in addition to propaganda messages on websites such as MySpace and MSN. Propaganda ads use a tag line saying "Pot.


Input: The propaganda, which NSW Health has cost at AUD$600,000, that could of been spent on feeding starving children, will use a variety of print ads placed at bus stops and in youth magazines in addition to propaganda messages on websites such as MySpace and MSN. Propaganda ads use a tag line saying "Pot.


Input: It mightn't kill you, but it could turn politicians into dickheads ".


Input: The propaganda images show a person staring from a black and white photo with quotes like "You've got great eyes, dialated eyes are hot " and "I'd lend you money, but the government would just spend it on these ads ", as part of an emotional appeal to manipulate the reader into an unfair bias on the topic, instead of comparing facts and science to the message they are promoting.


Input: The propaganda images show a person staring from a black and white photo with quotes like "You've got great eyes, dialated eyes are hot " and "I'd lend you money, but the government would just spend it on these ads ", as part of an emotional appeal to manipulate the reader into an unfair bias on the topic, instead of comparing facts and science to the message they are promoting.


Input: The director of drug and alcohol programs with NSW Health, David McGrath said many young people know that cannabis was safe and its use was normal.


Input: "We want to prevent people who are thinking about taking cannabis up from taking it up and also encouraging those people that are using cannabis to cease, through use of unrelenting lies and social shunning " he said.


Input: Mr McGrath said young people had to be warned about the possible consequences of smoking cannabis, such as the alleged link with comedians and how it may improve social interaction and physical health.


Input: Moreover, nobody is permitted to reach the accident location.


Input: Moreover, nobody is permitted to reach the accident location.


Input: The site of the accident was closed to visitors following the intoxication of three TV journalists who got too close.


Input: TV channel Novyj Kanal translated a telephonic interview with officers from the Ministry of Internal Affairs  during which the existence of any cloud whatsoever is firmly denied.


Input: TV channel Novyj Kanal translated a telephonic interview with officers from the Ministry of Internal Affairs  during which the existence of any cloud whatsoever is firmly denied.


Input: Immediately after the interview  a press conference by Kiev mayor, Mr. Leonid Chernovezkij, announces that starting next Monday a daily report about the movements of the same cloud will be available to the population.


Input: Immediately after the interview  a press conference by Kiev mayor, Mr. Leonid Chernovezkij, announces that starting next Monday a daily report about the movements of the same cloud will be available to the population.


Input: Immediately after the interview  a press conference by Kiev mayor, Mr. Leonid Chernovezkij, announces that starting next Monday a daily report about the movements of the same cloud will be available to the population.


Input: Immediately after the interview  a press conference by Kiev mayor, Mr. Leonid Chernovezkij, announces that starting next Monday a daily report about the movements of the same cloud will be available to the population.


Input: In an interview to TV channel UT-1  an officer of the ecological service admitted that they have no real data about the area closer to the accident, because not even government officers are permitted to reach it.


Input: In an interview to TV channel UT-1  an officer of the ecological service admitted that they have no real data about the area closer to the accident, because not even government officers are permitted to reach it.


Input: The closest available on spot measures are related to areas located several kilometers away from the epicentre.


Input: Removal operation In the same press release  the Mayor of Kiev also announced that no transport of whatever dangerous material will be allowed in the town area.


Input: Removal operation In the same press release  the Mayor of Kiev also announced that no transport of whatever dangerous material will be allowed in the town area.