wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We also apply our method to web-scraped Sinhala-English documents from ParaCrawl and find that our method improves MT performance by 1.2 BLEU over the current ParaCrawl document alignment method  .
Revised:  We also apply our method to web-scraped Sinhala-English documents from ParaCrawl and find that our method improves MT performance by 1.2 BLEU over the current ParaCrawl document alignment method used in the most recent ParaCrawl release. It also outperforms a comparable corpora method which uses the same multilingual embeddings, demonstrating that exploiting sentence order is beneficial even if the end goal is sentence-level bitext .
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Original:  We introduce  a novel MT training method that approximates the full space of possible translations by : sampling a paraphrase of the reference sentence from a paraphraser and training the MT model to predict the paraphraser's distribution over possible tokens.
Revised:  We introduce Simulated Multiple Reference Training (SMRT), a novel MT training method that approximates the full space of possible translations by : sampling a paraphrase of the reference sentence from a paraphraser and training the MT model to predict the paraphraser's distribution over possible tokens.
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Original:  We introduce  a novel MT training method that approximates the full space of possible translations by : sampling a paraphrase of the reference sentence from a paraphraser and training the MT model to predict the paraphraser's distribution over possible tokens.
Revised:  We introduce a novel MT training method that approximates the full space of possible translations by sampling a paraphrase of the reference sentence from a paraphraser and training the MT model to predict the paraphraser's distribution over possible tokens.
Edit intent: fluency
Original:  With an English paraphraser, we demonstrate the effectiveness of our method in low-resource settings , with gains of 1.2 to 7 BLEU .
Revised:  With an English paraphraser, we demonstrate the effectiveness of our method in low-resource settings , with gains of 1.2 to 7.0 BLEU. We also find SMRT is complementary to back-translation .
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Original:  This condition implies that public-health interventions are most likely to be undermined by increased economic activity near the peak of severe diseases  .
Revised:  This condition implies that public-health interventions are most likely to be undermined by increased economic activity near the peak of severe diseases . We also derive an explicit formula for the behavior of a heterogeneous population. A key difference from the homogeneous case is that high-risk individuals may choose to temporarily stop all activity when they are likely to encounter infected low-risk individuals .
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Original:  This condition implies that behavioral responses are most likely to undermine public-health interventions near the peak of severe diseases . We also derive an explicit formula for the behavior of a heterogeneous population. A key difference from the homogeneous case is that high-risk individuals may choose to temporarily stop all activity when they are likely to encounter infected low-risk individuals .
Revised:  This condition implies that behavioral responses are most likely to undermine public-health interventions near the peak of severe diseases .
Edit intent: coherence
Original: Adversarial training is effective in improving the robustness of neural networks . In NLP, languages are discrete in nature, separate tokens possess discrete semantics. Therefore, to incorporate adversarial trainingin sequence-level tasks, we introduce a novel training strategy: Text Adversarial Training with token-level perturbation.
Revised: Adversarial training is effective in improving the robustness of neural networks , while it cannot be easily adapted to natural language processing tasks since the embedding space is discrete. In natural language processing fields, virtual adversarial training is introduced since texts are discrete and cannot be perturbed by gradients directly. Alternatively, virtual adversarial training, which generates perturbations on the embedding space, is introduced in NLP tasks. Despite its success, existing virtual adversarial training methods generate perturbations roughly constrained by Frobenius normalization balls. To craft fine-grained perturbations, we propose a Token-Aware Virtual Adversarial Training method. We introduce a novel training strategy: Text Adversarial Training with token-level perturbation.
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Original:  Therefore, to incorporate adversarial trainingin sequence-level tasks, we introduce a novel training strategy: Text Adversarial Training with token-level perturbation. We fist craft perturbations that are initialized using a fine-grained token-level accumulated perturbations.
Revised:  Therefore, to incorporate adversarial trainingin sequence-level tasks, we introduce a novel training strategy: Text Adversarial Training with token-level accumulated perturbations.
Edit intent: coherence
Original:  We fist craft perturbations that are initialized using a fine-grained token-level accumulated perturbations. Then we constrain these perturbations considering that inputs are separate tokens, rather than constraining them under a naive normalization ball . We validate the effectiveness of such normalization method using large-scale Transformer-based language models. Experiments on GLUE benchmark and NER task show that our adversarial training strategy improves the performances on various tasks including text classification and sequence labeling .
Revised:  We fist craft perturbations that are initialized using a fine-grained token-level accumulated perturbation vocabulary to initialize the perturbations better and use a token-level normalization ball to constrain these perturbations pertinently. Experiments show that our adversarial training strategy improves the performances on various tasks including text classification and sequence labeling .
Edit intent: clarity
Original:  Experiments on GLUE benchmark and NER task show that our adversarial training strategy improves the performances on various tasks including text classification and sequence labeling .
Revised:  Experiments on GLUE benchmark and NER task show that our method improves the performance of pre-trained models such as BERT and ALBERT in various tasks by a considerable margin. The proposed method improves the score of the GLUE benchmark from 78.3 to 80.9 using BERT model and it also enhances the performance of sequence labeling and text classification tasks .
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Original:  It is a challenging problem that requires syntactic and semantic information at sentence or discourse levels, which may be captured by deep language models such as BERT (Devlin et al., 2019).
Revised:  It is a challenging problem that requires syntactic and semantic information at sentence or discourse levels, which may be captured by deep contextualized language models (LMs) such as BERT (Devlin et al., 2019).
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Original:  The software utilises a flexible plugin system that can be adapted to diverse applications such as counting mitotic figures with the screening mode, finding false annotations on a novel validation view, or using the latest deep learning image analysis technologies.
Revised:  The software utilises a flexible plugin system that can be adapted to diverse applications such as counting mitotic figures with a screening mode, finding false annotations on a novel validation view, or using the latest deep learning image analysis technologies.
Edit intent: fluency
Original:  EXACT is freely available and has been applied successfully to a broad range of annotation tasks already , including highly diverse applications like deep learning supported cytology grading , interdisciplinary multi-centre whole slide image tumour annotation, and highly specialised whale sound spectroscopy clustering.
Revised:  EXACT is freely available and has already been successfully applied to a broad range of annotation tasks already , including highly diverse applications like deep learning supported cytology grading , interdisciplinary multi-centre whole slide image tumour annotation, and highly specialised whale sound spectroscopy clustering.
Edit intent: fluency
Original:  EXACT is freely available and has been applied successfully to a broad range of annotation tasks already , including highly diverse applications like deep learning supported cytology grading , interdisciplinary multi-centre whole slide image tumour annotation, and highly specialised whale sound spectroscopy clustering.
Revised:  EXACT is freely available and has been applied successfully to a broad range of annotation tasks , including highly diverse applications like deep learning supported cytology grading , interdisciplinary multi-centre whole slide image tumour annotation, and highly specialised whale sound spectroscopy clustering.
Edit intent: coherence
Original:  EXACT is freely available and has been applied successfully to a broad range of annotation tasks already , including highly diverse applications like deep learning supported cytology grading , interdisciplinary multi-centre whole slide image tumour annotation, and highly specialised whale sound spectroscopy clustering.
Revised:  EXACT is freely available and has been applied successfully to a broad range of annotation tasks already , including highly diverse applications like deep learning supported cytology scoring , interdisciplinary multi-centre whole slide image tumour annotation, and highly specialised whale sound spectroscopy clustering.
Edit intent: clarity
Original: We propose a novel methodology for analyzing the encoding of grammatical structure in neural language models through transfer learning. We test how a language model can leverage its internal representations to transfer knowledge across languages and symbol systems. We train LSTMs on non-linguistic , structured data and test their performance on human language to assess which kinds of data induce generalizable encodings that LSTMs can use for natural language.
Revised: We propose a novel methodology for analyzing the encoding of grammatical structure in neural language models . We train LSTMs on non-linguistic , structured data and test their performance on human language to assess which kinds of data induce generalizable encodings that LSTMs can use for natural language.
Edit intent: clarity
Original:  We find that models trained on structured data such as music and Java codehave internal representations that help in modelling human language, and that, surprisingly, adding minimal amounts of structure to the training data makes a large difference in transfer to natural language . Further experiments on transfer between human languages show that zero-shot performance on a test language is highly correlated with  syntactic similarity to the training language, even after removing any vocabulary overlap.
Revised:  We find that training on non-linguistic data with latent structure (MIDI music or Java code) improves test performance on natural language, despite no overlap in surface form or vocabulary. Training on artificial languages containing recursion (hierarchical structure) also improves performance on natural language, again with no vocabulary overlap. Surprisingly, training on artificial languages consisting of sets of separated pairs of words, but with no recursion, improves performance on natural language as well as recursive languages do. Experiments on transfer between human languages show that zero-shot performance on a test language is highly correlated with syntactic similarity to the training language, even after removing any vocabulary overlap.
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Original:  Further experiments on transfer between human languages show that zero-shot performance on a test language is highly correlated with  syntactic similarity to the training language, even after removing any vocabulary overlap. This suggests that the internal representations induced from natural languages are typologically coherent: they encode the features and differences outlined in typological studies .
Revised:  Further experiments on transfer between human languages show that zero-shot performance on a test language is highly correlated with syntactic similarity to the training language, suggesting that representations induced from natural languages are typologically coherent: they encode the features and differences outlined in typological studies .
Edit intent: clarity
Original:  Training on artificial languages containing recursion (hierarchical structure) also improves performance on natural language, again with no vocabulary overlap . Surprisingly, training  on artificial languages consisting of sets of separated pairs of words, but with no recursion, improves performance on natural language as well as recursive languages do.
Revised:  To pinpoint the kinds of abstract structure that models may be encoding to lead to this improvement, we run similar experiments with two artificial parentheses languages: one which has a hierarchical recursive structure, and a control which has paired tokens but no recursion . Surprisingly, training on artificial languages consisting of sets of separated pairs of words, but with no recursion, improves performance on natural language as well as recursive languages do.
Edit intent: clarity
Original:  Training on artificial languages containing recursion (hierarchical structure) also improves performance on natural language, again with no vocabulary overlap . Surprisingly, training  on artificial languages consisting of sets of separated pairs of words, but with no recursion, improves performance on natural language as well as recursive languages do.
Revised:  Training on artificial languages containing recursion (hierarchical structure) also improves performance on natural language, again with no vocabulary overlap . Surprisingly, training a model on either of these artificial languages leads to the same substantial gains when testing on artificial languages consisting of sets of separated pairs of words, but with no recursion, improves performance on natural language as well as recursive languages do.
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Original:  Training on artificial languages containing recursion (hierarchical structure) also improves performance on natural language, again with no vocabulary overlap . Surprisingly, training  on artificial languages consisting of sets of separated pairs of words, but with no recursion, improves performance on natural language as well as recursive languages do.
Revised:  Training on artificial languages containing recursion (hierarchical structure) also improves performance on natural language, again with no vocabulary overlap . Surprisingly, training on natural language as well as recursive languages do.
Edit intent: coherence
Original:  Training on artificial languages containing recursion (hierarchical structure) also improves performance on natural language, again with no vocabulary overlap . Surprisingly, training  on artificial languages consisting of sets of separated pairs of words, but with no recursion, improves performance on natural language as well as recursive languages do. Experiments on transfer between natural languages  show that zero-shot performance on a test language is highly correlated with typological syntactic similarity to the training language, suggesting that representations induced from natural languages correspond to the cross-linguistic syntactic properties studied in linguistic typology .
Revised:  Training on artificial languages containing recursion (hierarchical structure) also improves performance on natural language, again with no vocabulary overlap . Surprisingly, training on artificial languages consisting of sets of separated pairs of words, but with no recursion, improves performance on natural language . Further experiments on transfer between natural languages show that zero-shot performance on a test language is highly correlated with typological syntactic similarity to the training language, suggesting that representations induced from natural languages correspond to the cross-linguistic syntactic properties studied in linguistic typology .
Edit intent: clarity
Original:  Experiments on transfer between natural languages  show that zero-shot performance on a test language is highly correlated with typological syntactic similarity to the training language, suggesting that representations induced from natural languages correspond to the cross-linguistic syntactic properties studied in linguistic typology .
Revised:  Experiments on transfer between natural languages controlling for vocabulary overlap show that zero-shot performance on a test language is highly correlated with typological syntactic similarity to the training language, suggesting that representations induced from natural languages correspond to the cross-linguistic syntactic properties studied in linguistic typology .
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Original:  Experiments on transfer between natural languages  show that zero-shot performance on a test language is highly correlated with typological syntactic similarity to the training language, suggesting that representations induced from natural languages correspond to the cross-linguistic syntactic properties studied in linguistic typology .
Revised:  Experiments on transfer between natural languages show that zero-shot performance on a test language is highly correlated with typological syntactic similarity to the training language, suggesting that representations induced by pre-training correspond to the cross-linguistic syntactic properties studied in linguistic typology .
Edit intent: meaning-changed
Original:  Experiments on transfer between natural languages  show that zero-shot performance on a test language is highly correlated with typological syntactic similarity to the training language, suggesting that representations induced from natural languages correspond to the cross-linguistic syntactic properties studied in linguistic typology .
Revised:  Experiments on transfer between natural languages show that zero-shot performance on a test language is highly correlated with typological syntactic similarity to the training language, suggesting that representations induced from natural languages correspond to the cross-linguistic syntactic properties .
Edit intent: clarity
Original: In this study, we hypothesize that when the distribution of the answer positions is highly skewed in the training set (e.g., answers lie only in the k-th sentence of each passage), QA models predicting answers as positions  learn spurious positional cues and fail to give answers in different positions.
Revised: In this study, we hypothesize that when the distribution of the answer positions is highly skewed in the training set (e.g., answers lie only in the k-th sentence of each passage), QA models predicting answers as positions can learn spurious positional cues and fail to give answers in different positions.
Edit intent: clarity
Original:  Among them, we found that using the prior distribution of answer positions as a bias model is very effective at reducing position bias  recovering the performance of BERT from 35.24\% to 81.17 \% when trained on a biased SQuAD dataset.
Revised:  Among them, we found that using the prior distribution of answer positions as a bias model is very effective at reducing position bias , recovering the performance of BERT from 35.24\% to 81.17 \% when trained on a biased SQuAD dataset.
Edit intent: fluency
Original:  Among them, we found that using the prior distribution of answer positions as a bias model is very effective at reducing position bias  recovering the performance of BERT from 35.24\% to 81.17 \% when trained on a biased SQuAD dataset.
Revised:  Among them, we found that using the prior distribution of answer positions as a bias model is very effective at reducing position bias recovering the performance of BERT from 37.48\% to 81.64 \% when trained on a biased SQuAD dataset.
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Original: Neural conversation models have shown the power to produce more meaningful and engaging responses given external knowledge.Specifically, the knowledge we experiment on is in textual form, for example, a personality description.
Revised: Dialogue generation has been successfully learned from scratch by neural networks, but tends to produce the same general response, e.g., "what are you talking about?", in many conversations. To reduce this homogeneity, external knowledge such as the speaker's profile and domain knowledge is applied as an additional condition to diversify a model's output. The required knowledge to develop an effective conversation, however, is not always available, which is different from prior work's assumption that a model always has acquired sufficient knowledge we experiment on is in textual form, for example, a personality description.
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Original: Specifically, the knowledge we experiment on is in textual form, for example, a personality description. Despite the success of training and testing with external knowledge , in reality, we do not always have sufficient background knowledge about the discussed topic. Therefore, it is also crucial to have the models generate captivating responses without external knowledge.
Revised: Specifically, the knowledge before chatting. This problem can be detrimental when applying a dialogue model like this chatting online with unconstrained people and topics, because the model does not have the needed knowledge.
Edit intent: coherence
Original:  To achieve this , we propose a unified training method, Decoupling, which induces a knowledge-related sentence and couples it with the dialogue history to generate a response in an unsupervised fashion. Its effect is further analyzed by testing the models with no knowledge, partial and full text of the knowledge. Empirically, we observed that the variance of the performance given different amounts of knowledge is significant. Also, our method performs more closely to the supervised method (the upper bound) than the baselines .
Revised:  To achieve this , we propose InjK, which is a two-stage approach to inject knowledge into a dialogue generation model. First, we train a large-scale language model and query it as textual knowledge. Second, we frame a dialogue generation model to sequentially generate textual knowledge and a corresponding response. Empirically, when a dialogue generation model can only access limited knowledge, our method outperforms prior work by producing more coherent and informative responses .
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Original: In adversarial testing, we pose hard generalization tasks in order to gain insights into the solutions found by our models. What properties must a system have in order to succeed at these hard behavioral tasks? We argue that an essential factor is modular internal structure. Our central contribution is a new experimental method called 'interchange interventions', in which systematic manipulations of model-internal states are related to causal effects on their outputs, thereby allowing us to identify modular structure. Our work is grounded empirically in a new challenge Natural Language Inference dataset designed to assess systems on their ability to reason about entailment and negation.
Revised: We address whether neural models for Natural Language Inference (NLI) can learn the compositional interactions between lexical entailment and negation, using four methods: the behavioral evaluation methods of (1) challenge test sets and (2) systematic generalization tasks, and the structural evaluation methods of (3) probes and (4) interventions. To facilitate this holistic evaluation, we present Monotonicity NLI (MoNLI), a new naturalistic dataset focused on lexical entailment and negation.
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