wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We conduct comprehensive experiments in dialogue state tracking, context-to-text, and end-to-end settings on the MultiWOZ2.1 benchmark, achieving superior performance over competitive baselines in all tasks. Our code and models will be released .
Revised:  We conduct comprehensive experiments in dialogue state tracking, context-to-text, and end-to-end settings on the MultiWOZ2.1 benchmark, achieving superior performance over competitive baselines .
Edit intent: clarity
Original:  To address these issues , we propose a novel multilingual task adaptation approach based on recent work in parameter-efficient transfer learning, which allows for an easy but effective integration of existing linguistic typology features into the parsing network.
Revised:  To address this , we propose a novel multilingual task adaptation approach based on recent work in parameter-efficient transfer learning, which allows for an easy but effective integration of existing linguistic typology features into the parsing network.
Edit intent: coherence
Original:  To address these issues , we propose a novel multilingual task adaptation approach based on recent work in parameter-efficient transfer learning, which allows for an easy but effective integration of existing linguistic typology features into the parsing network.
Revised:  To address these issues , we propose a novel multilingual task adaptation approach based on contextual parameter generation and adapter modules. This approach enables to learn adapters via language embeddings while sharing model parameters across languages. It also allows for an easy but effective integration of existing linguistic typology features into the parsing network.
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Original:  The resulting parser, UDapter, consistently outperforms strong monolingual and multilingual baselines on  both high-resource and low-resource (zero-shot) languages, setting a new state of the art in multilingual UD parsing .
Revised:  The resulting parser, UDapter, outperforms strong monolingual and multilingual baselines on both high-resource and low-resource (zero-shot) languages, setting a new state of the art in multilingual UD parsing .
Edit intent: clarity
Original:  The resulting parser, UDapter, consistently outperforms strong monolingual and multilingual baselines on  both high-resource and low-resource (zero-shot) languages, setting a new state of the art in multilingual UD parsing .
Revised:  The resulting parser, UDapter, consistently outperforms strong monolingual and multilingual baselines on the majority of both high-resource and low-resource (zero-shot) languages, setting a new state of the art in multilingual UD parsing .
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Original:  The resulting parser, UDapter, consistently outperforms strong monolingual and multilingual baselines on  both high-resource and low-resource (zero-shot) languages, setting a new state of the art in multilingual UD parsing .
Revised:  The resulting parser, UDapter, consistently outperforms strong monolingual and multilingual baselines on both high-resource and low-resource (zero-shot) languages, showing the success of the proposed adaptation approach .
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Original: Background In the absence of a vaccine or effective treatment for COVID-19, countries have adopted Non-Pharmaceutical Interventions (NPIs) such as social distancing and full lockdown. An objective and quantitative means of monitoring the impact and response of these interventions at a local level is urgently required. Here we explore the utility of the recently developed open-source mobile health platform RADAR-base as a toolbox to  test the effect and response to NPIs aimed at limiting the spread of COVID-19.
Revised: We aimed to explore the utility of the recently developed open-source mobile health platform RADAR-base as a toolbox to test the effect and response to NPIs aimed at limiting the spread of COVID-19.
Edit intent: coherence
Original:  We derived features from the phone and wearable device for length of time spent at home,  distance travelled from home,  heart rate, sleep , and patterns of phone use.
Revised:  We derived features from the phone and wearable device for length of time spent at home, distance travelled from home, maximum number of Bluetooth-enabled nearby devices (as a proxy for physical distancing), step count, average heart rate, sleep , and patterns of phone use.
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Original:  We derived features from the phone and wearable device for length of time spent at home,  distance travelled from home,  heart rate, sleep , and patterns of phone use. We visualised data using time series plots and performed statistical tests to assess differences in behaviour during baseline, pre-, and post-lockdown periods.
Revised:  We derived features from the phone and wearable device for length of time spent at home, distance travelled from home, heart rate, sleep duration, bedtime, phone unlock duration, and social app use duration. We performed Kruskal-Wallis tests followed by post-hoc Dunns tests to assess differences in behaviour during baseline, pre-, and post-lockdown periods.
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Original:  We visualised data using time series plots and performed statistical tests to assess differences in behaviour during baseline, pre-, and post-lockdown periods. Findings We found significant changes in behaviours between baseline/pre-lockdown and post-lockdown for all features except total sleep duration. In general, participants spent more time at home, travelled much less, and were more active on their phones, interacting with others by using social apps.
Revised:  We visualised data using time series plots and performed statistical tests to assess differences in behaviour during baseline, pre-, and during-lockdown periods. We also studied behavioural differences by age, gender, body mass index (BMI), and educational background. We were able to quantify expected changes in time spent at home, travelled much less, and were more active on their phones, interacting with others by using social apps.
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Original:  In general, participants spent more time at home, travelled much less, and were more active on their phones, interacting with others by using social apps.
Revised:  In general, participants spent more time at home, distance travelled, and the number of nearby Bluetooth-enabled devices between pre- and during-lockdown periods. We saw reduced sociality as measured through mobility features, and increased virtual sociality through phone usage. People were more active on their phones, interacting with others by using social apps.
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Original:  In general, participants spent more time at home, travelled much less, and were more active on their phones, interacting with others by using social apps. Nevertheless, the response across nations differed with Denmark showing attenuated changes in behaviour. Interpretation Differences in the extracted features by country may reflect variations in communication and implementation of different NPIs as well as cultural differences. We have demonstrated that generalised open-source mobile health monitoring platforms such as RADAR-base which leverages data from wearables and mobile technologies are valuable tools for helping understand the behavioural impact of public health interventions implemented in response to  infectious outbreaks such as COVID-19.
Revised:  In general, participants spent more time at home, travelled much less, and were more active on their phones, spending more time using social media apps, particularly around major news events. Furthermore, participants had lower heart rate, went to bed later, and slept more. We also found that young people had longer homestay than older people during lockdown and fewer daily steps. Although there was no significant difference between the high and low BMI groups in time spent at home, the low BMI group walked more. RADAR-base which leverages data from wearables and mobile technologies are valuable tools for helping understand the behavioural impact of public health interventions implemented in response to infectious outbreaks such as COVID-19.
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Original:  We have demonstrated that generalised open-source mobile health monitoring platforms such as RADAR-base which leverages data from wearables and mobile technologies are valuable tools for helping understand the behavioural impact of public health interventions implemented in response to  infectious outbreaks such as COVID-19.
Revised:  We have demonstrated that generalised open-source mobile health monitoring platforms such as RADAR-base can be used to rapidly quantify and provide a holistic view of behavioural changes in response to infectious outbreaks such as COVID-19.
Edit intent: clarity
Original:  We have demonstrated that generalised open-source mobile health monitoring platforms such as RADAR-base which leverages data from wearables and mobile technologies are valuable tools for helping understand the behavioural impact of public health interventions implemented in response to  infectious outbreaks such as COVID-19.
Revised:  We have demonstrated that generalised open-source mobile health monitoring platforms such as RADAR-base which leverages data from wearables and mobile technologies are valuable tools for helping understand the behavioural impact of public health interventions implemented in response to public health interventions as a result of infectious outbreaks such as COVID-19.
Edit intent: clarity
Original:  However, prior pretraining work has been limited to only instructional videos , a domain that, a priori, we expect  to be relatively "easy:" speakers in instructional videos will often reference the literal objects/actions being depicted.
Revised:  However, prior pretraining work has been limited to only instructional videos ; a priori, we expect to be relatively "easy:" speakers in instructional videos will often reference the literal objects/actions being depicted.
Edit intent: clarity
Original:  However, prior pretraining work has been limited to only instructional videos , a domain that, a priori, we expect  to be relatively "easy:" speakers in instructional videos will often reference the literal objects/actions being depicted.
Revised:  However, prior pretraining work has been limited to only instructional videos , a domain that, a priori, we expect this domain to be relatively "easy:" speakers in instructional videos will often reference the literal objects/actions being depicted.
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Original:  We find that visual-textual grounding is indeed possible across previously unexplored video categories, and that pretraining on a more diverse set still results in representations that generalize to both non-instructional and instructional domains.
Revised:  We find that visual-textual grounding is indeed possible across previously unexplored video categories, and that pretraining on a more diverse set results in representations that generalize to both non-instructional and instructional domains.
Edit intent: clarity
Original:  To this end, we construct a multilingual NLU corpus , MultiATIS++, by extending the Multilingual ATIS corpus to nine languages across various language families. We use the corpusto explore various cross-lingual transfer methods focusing on the zero-shot setting and leveraging MT for language expansion . Results show that our soft-alignment method significantly improves slot F1 over strong baselines on most languages .
Revised:  To this end, we construct a multilingual NLU corpus , MultiATIS++, by extending the Multilingual ATIS corpus to nine languages across four language families, and evaluate our method using the corpus . Results show that our soft-alignment method significantly improves slot F1 over strong baselines on most languages .
Edit intent: clarity
Original:  We use the corpusto explore various cross-lingual transfer methods focusing on the zero-shot setting and leveraging MT for language expansion . Results show that our soft-alignment method significantly improves slot F1 over strong baselines on most languages .
Revised:  We use the corpusto explore various cross-lingual transfer methods focusing on the zero-shot setting and leveraging MT for language expansion . Results show that our method outperforms a simple label projection method using fast-align on most languages .
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Original:  We use the corpusto explore various cross-lingual transfer methods focusing on the zero-shot setting and leveraging MT for language expansion . Results show that our soft-alignment method significantly improves slot F1 over strong baselines on most languages . In addition, our experiments show the strength of using multilingual BERT for both cross-lingual training and zero-shot transfer .
Revised:  We use the corpusto explore various cross-lingual transfer methods focusing on the zero-shot setting and leveraging MT for language expansion . Results show that our soft-alignment method significantly improves slot F1 over strong baselines on most languages , and achieves competitive performance to the more complex, state-of-the-art projection method with only half of the training time. We release our MultiATIS++ corpus to the community to continue future research on cross-lingual NLU .
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Original:  In this paper, we show that elastic weight consolidation (EWC) allows fine-tuning of models to mitigate biases for NLI and fact verification while being less susceptible to catastrophic URLetting.
Revised:  In this paper, we show that elastic weight consolidation (EWC) allows fine-tuning of models to mitigate biases while being less susceptible to catastrophic URLetting.
Edit intent: coherence
Original:  In our evaluation on fact verification systems , we show that fine-tuning with EWC Pareto dominates standard fine-tuning, yielding models  lower levels of URLetting on the original task for equivalent gains in accuracy on the fine-tuned task.
Revised:  In our evaluation on fact verification and NLI stress tests , we show that fine-tuning with EWC Pareto dominates standard fine-tuning, yielding models lower levels of URLetting on the original task for equivalent gains in accuracy on the fine-tuned task.
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Original:  In our evaluation on fact verification systems , we show that fine-tuning with EWC Pareto dominates standard fine-tuning, yielding models  lower levels of URLetting on the original task for equivalent gains in accuracy on the fine-tuned task.
Revised:  In our evaluation on fact verification systems , we show that fine-tuning with EWC dominates standard fine-tuning, yielding models lower levels of URLetting on the original task for equivalent gains in accuracy on the fine-tuned task.
Edit intent: clarity
Original:  In our evaluation on fact verification systems , we show that fine-tuning with EWC Pareto dominates standard fine-tuning, yielding models  lower levels of URLetting on the original task for equivalent gains in accuracy on the fine-tuned task.
Revised:  In our evaluation on fact verification systems , we show that fine-tuning with EWC Pareto dominates standard fine-tuning, yielding models with lower levels of URLetting on the original task for equivalent gains in accuracy on the fine-tuned task.
Edit intent: fluency
Original:  In our evaluation on fact verification systems , we show that fine-tuning with EWC Pareto dominates standard fine-tuning, yielding models  lower levels of URLetting on the original task for equivalent gains in accuracy on the fine-tuned task.
Revised:  In our evaluation on fact verification systems , we show that fine-tuning with EWC Pareto dominates standard fine-tuning, yielding models lower levels of URLetting on the original (biased) dataset for equivalent gains in accuracy on the fine-tuned task.
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Original:  In our evaluation on fact verification systems , we show that fine-tuning with EWC Pareto dominates standard fine-tuning, yielding models  lower levels of URLetting on the original task for equivalent gains in accuracy on the fine-tuned task. Additionally, we show that systems trained on NLI can be fine-tuned to improve their accuracy on stress test challenge tasks with minimal loss in accuracy on the MultiNLI datasetdespite greater domain shift .
Revised:  In our evaluation on fact verification systems , we show that fine-tuning with EWC Pareto dominates standard fine-tuning, yielding models lower levels of URLetting on the original task for equivalent gains in accuracy on the fine-tuning (unbiased) dataset .
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Original: Deep neural scoring models have recently been shown to improve ranking quality on a number of benchmarks (Guo et al., 2016; Daiet al., 2018; MacAvaney et al., 2019; Yanget al., 2019a). However, these methods rely on underlying ad-hoc retrieval systems to generate candidates for scoring, which are rarely neural themselves (Zamani et al., 2018). Re-cent work has shown that the performance of ad-hoc neural retrieval systems can be competitive with a number of baselines (Zamani et al.,2018), potentially leading the way to full end-to-end neural retrieval. A major road-block to the adoption of ad-hoc retrieval models is that they require large supervised training sets to surpass classic term-based techniques, which can be developed from raw corpora.
Revised: A major obstacle to the wide-spread adoption of neural retrieval models is that they require large supervised training sets to surpass classic term-based techniques, which can be developed from raw corpora.
Edit intent: clarity
Original:  In this paper, we pro-pose an approach to zero-shot learning (Xianet al., 2018) for ad-hoc retrieval models that relies on synthetic query generation . Crucially, the query generation system is trained on general domain data, but is applied to documents in the targeted domain.
Revised:  In this paper, we propose an approach to zero-shot learning (Xianet al., 2018) for ad-hoc retrieval models that relies on synthetic query generation . Crucially, the query generation system is trained on general domain data, but is applied to documents in the targeted domain.
Edit intent: fluency
Original:  In this paper, we pro-pose an approach to zero-shot learning (Xianet al., 2018) for ad-hoc retrieval models that relies on synthetic query generation . Crucially, the query generation system is trained on general domain data, but is applied to documents in the targeted domain.
Revised:  In this paper, we pro-pose an approach to zero-shot learning for passage retrieval that uses synthetic question generation to close this gap. The question generation system is trained on general domain data, but is applied to documents in the targeted domain.
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Original:  This allows us to create arbitrarily large, yet noisy, query-document relevance pairs that are domain targeted. On a number of benchmarks, we show that this is an effective strategy for building neural retrieval models for specialised domains .
Revised:  This allows us to create arbitrarily large, yet noisy, query-document relevance pairs that are domain specific. Furthermore, when this is coupled with a simple hybrid term-neural model, first-stage retrieval performance can be improved further. Empirically, we show that this is an effective strategy for building neural retrieval models for specialised domains .
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Original:  In this paper, we propose a counterfactual off-policy method to learn on a better synthesis of data. It takes advantage of a real responseto infer an alternative that was not takenusing a structural casual model. Learning on the counterfactual responses  helps to explore the high-reward area of the  response space.
Revised:  In this paper, we propose to explore potential responses by counterfactual reasoning. Given an observed response, the counterfactual reasoning model automatically infers the outcome of an alternative policy that could have been taken. The resulting counterfactual response synthesized in hindsight is of higher quality than the response synthesized from scratch. Training on the counterfactual responses helps to explore the high-reward area of the response space.
Edit intent: clarity
Original: Arabic 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). A few multilingual pre-trained models have been proposed and show good performance for Arabic, however, most experiment results are reported on language understanding tasks, such as natural language inference, question answering and sentiment analysis.
Revised: Multilingual pre-trained models have been proposed and show good performance for Arabic, however, most experiment results are reported on language understanding tasks, such as natural language inference, question answering and sentiment analysis.
Edit intent: clarity
Original:  A few multilingual pre-trained models have been proposed and show good performance for Arabic, however, most experiment results are reported on language understanding tasks, such as natural language inference, question answering and sentiment analysis. Their performance on the IEtasks is less known, in particular, the cross-lingual transfer capability from English to Arabic .
Revised:  A few multilingual pre-trained Transformers, such as mBERT (Devlin et al., 2019) and XLM-RoBERTa (Conneau et al., 2020a), have been shown to enable the effective cross-lingual transfer capability from English to Arabic .
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Original:  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 learningon various IE tasks.
Revised:  In this paper , we pre-train a Gigaword-based bilingual language model (GigaBERT) to study these two distant languages as well as zero-short transfer learningon various IE tasks.
Edit intent: clarity
Original:  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 learningon various IE tasks. Our GigaBERT outperforms multilingual BERT and and monolingual AraBERT on these tasks, in both supervised and zero-shot learning settings.
Revised:  In this work , we pre-train a customized bilingual BERT, dubbed GigaBERT, that is designed specifically for Arabic NLP and English-to-Arabic zero-shot transfer learning. We study GigaBERT's effectiveness on zero-short transfer across four IE tasks: named entity recognition, part-of-speech tagging, argument role labeling, and relation extraction. Our best model significantly outperforms mBERT, XLM-RoBERTa, and AraBERT (Antoun et al., 2020) in both the supervised and zero-shot learning settings.
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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:  Our method improves downstream MT performance on 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 .
Edit intent: clarity
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 .
Edit intent: fluency