wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We analyze two such datasets ( Natural Questions and TyDi QA ) to identify remaining headrooms : paragraph selection and answerability classification , i.e. determining whether the paired evidence document contains the answer to the query or not.
Revised:  We analyze two such datasets ( Natural Questions and TyDi QA . Our controlled experiments suggest two headrooms -- paragraph selection and answerability classification , i.e. determining whether the paired evidence document contains the answer to the query or not.
Edit intent: meaning-changed
Original:  We analyze two such datasets ( Natural Questions and TyDi QA ) to identify remaining headrooms : paragraph selection and answerability classification , i.e. determining whether the paired evidence document contains the answer to the query or not.
Revised:  We analyze two such datasets ( Natural Questions and TyDi QA ) to identify remaining headrooms : paragraph selection and answerability prediction , i.e. determining whether the paired evidence document contains the answer to the query or not.
Edit intent: clarity
Original:  We analyze two such datasets ( Natural Questions and TyDi QA ) to identify remaining headrooms : paragraph selection and answerability classification , i.e. determining whether the paired evidence document contains the answer to the query or not.
Revised:  We analyze two such datasets ( Natural Questions and TyDi QA ) to identify remaining headrooms : paragraph selection and answerability classification , i.e. whether the paired evidence document contains the answer to the query or not.
Edit intent: coherence
Original:  In other words, given a gold paragraph and knowing whether it contains an answer or not, models easily outperform a single annotatorin both datasets. After identifying unanswerability as a bottleneck, we further inspect what makes questions unanswerable. Our study points to avenues for future research  , both for dataset creation and model development.
Revised:  In other words, given a gold paragraph and knowing whether it contains an answer or not, models easily outperform a human annotator. However, predicting answerability itself remains challenging. We manually annotate 800 unanswerable examples across six languages on what makes them challenging to answer. With this new data, we conduct per-category answerability prediction, revealing issues in the current dataset collection as well as task formulation. Together, our study points to avenues for future research , both for dataset creation and model development.
Edit intent: meaning-changed
Original:  However, existing approaches usually finetune pre-trained models directly on a separate downstream Chinese Word Segmentation corpus. These recent methods don't fully utilize the prior knowledge of existing segmentation corpora, and don't regard the discrepancy between the pre-training tasks and the downstream Chinese Word Segmentation tasks.
Revised:  However, PTMs used in previous works usually adopt language modeling as pre-training tasks, lacking task-specific prior segmentation knowledge and ignoring the discrepancy between the pre-training tasks and the downstream Chinese Word Segmentation tasks.
Edit intent: clarity
Original:  which can be abbreviated as PTM-CWS. PTM-CWS model employs a unified architecture for different segmentation criteria, and is pre-trained on a joint multi-criteria corpus with meta learning algorithm  . Empirical results show that our PTM-CWS model can utilize the existing prior segmentation knowledge , reduce the discrepancy between the pre-training tasks and the downstream Chinese Word Segmentation tasks, and achieve new state-of-the-art performance on twelve Chinese Word Segmentation corpora .
Revised:  which can be abbreviated as PTM-CWS. PTM-CWS model employs a unified architecture and incorporates meta learning algorithm . Empirical results show that our PTM-CWS model can utilize the existing prior segmentation knowledge , reduce the discrepancy between the pre-training tasks and the downstream Chinese Word Segmentation tasks, and achieve new state-of-the-art performance on twelve Chinese Word Segmentation corpora .
Edit intent: clarity
Original:  and is pre-trained on a joint multi-criteria corpus with meta learning algorithm  . Empirical results show that our PTM-CWS model can utilize the existing prior segmentation knowledge , reduce the discrepancy between the pre-training tasks and the downstream Chinese Word Segmentation tasks, and achieve new state-of-the-art performance on twelve Chinese Word Segmentation corpora .
Revised:  and is pre-trained on a joint multi-criteria corpus with meta learning algorithm into a multi-criteria pre-training task . Empirical results show that our PTM-CWS model can utilize the existing prior segmentation knowledge , reduce the discrepancy between the pre-training tasks and the downstream Chinese Word Segmentation tasks, and achieve new state-of-the-art performance on twelve Chinese Word Segmentation corpora .
Edit intent: meaning-changed
Original:  and is pre-trained on a joint multi-criteria corpus with meta learning algorithm  . Empirical results show that our PTM-CWS model can utilize the existing prior segmentation knowledge , reduce the discrepancy between the pre-training tasks and the downstream Chinese Word Segmentation tasks, and achieve new state-of-the-art performance on twelve Chinese Word Segmentation corpora .
Revised:  and is pre-trained on a joint multi-criteria corpus with meta learning algorithm . Empirical results show that METASEG could utilize common prior segmentation knowledge , reduce the discrepancy between the pre-training tasks and the downstream Chinese Word Segmentation tasks, and achieve new state-of-the-art performance on twelve Chinese Word Segmentation corpora .
Edit intent: meaning-changed
Original:  and is pre-trained on a joint multi-criteria corpus with meta learning algorithm  . Empirical results show that our PTM-CWS model can utilize the existing prior segmentation knowledge , reduce the discrepancy between the pre-training tasks and the downstream Chinese Word Segmentation tasks, and achieve new state-of-the-art performance on twelve Chinese Word Segmentation corpora .
Revised:  and is pre-trained on a joint multi-criteria corpus with meta learning algorithm . Empirical results show that our PTM-CWS model can utilize the existing prior segmentation knowledge from different existing criteria and alleviate the discrepancy between the pre-training tasks and the downstream Chinese Word Segmentation tasks, and achieve new state-of-the-art performance on twelve Chinese Word Segmentation corpora .
Edit intent: meaning-changed
Original:  and is pre-trained on a joint multi-criteria corpus with meta learning algorithm  . Empirical results show that our PTM-CWS model can utilize the existing prior segmentation knowledge , reduce the discrepancy between the pre-training tasks and the downstream Chinese Word Segmentation tasks, and achieve new state-of-the-art performance on twelve Chinese Word Segmentation corpora .
Revised:  and is pre-trained on a joint multi-criteria corpus with meta learning algorithm . Empirical results show that our PTM-CWS model can utilize the existing prior segmentation knowledge , reduce the discrepancy between pre-trained models and downstream CWS tasks. Besides, METASEG can achieve new state-of-the-art performance on twelve Chinese Word Segmentation corpora .
Edit intent: coherence
Original:  and is pre-trained on a joint multi-criteria corpus with meta learning algorithm  . Empirical results show that our PTM-CWS model can utilize the existing prior segmentation knowledge , reduce the discrepancy between the pre-training tasks and the downstream Chinese Word Segmentation tasks, and achieve new state-of-the-art performance on twelve Chinese Word Segmentation corpora .
Revised:  and is pre-trained on a joint multi-criteria corpus with meta learning algorithm . Empirical results show that our PTM-CWS model can utilize the existing prior segmentation knowledge , reduce the discrepancy between the pre-training tasks and the downstream Chinese Word Segmentation tasks, and achieve new state-of-the-art performance on twelve widely-used CWS datasets and significantly improve model performance in low-resource settings .
Edit intent: meaning-changed
Original:  In this work, we describe a very simple approach for joint entity and relation extraction, and establish the new state-of-the-art on standard benchmarks (ACE04, ACE05 , and SciERC)  .
Revised:  In this work, we present a simple pipelined approach for entity and relation extraction, and establish the new state-of-the-art on standard benchmarks (ACE04, ACE05 , and SciERC) .
Edit intent: meaning-changed
Original:  In this work, we describe a very simple approach for joint entity and relation extraction, and establish the new state-of-the-art on standard benchmarks (ACE04, ACE05 , and SciERC)  .
Revised:  In this work, we describe a very simple approach for joint entity and relation extraction, and establish the new state-of-the-art on standard benchmarks (ACE04, ACE05 and SciERC) .
Edit intent: fluency
Original:  In this work, we describe a very simple approach for joint entity and relation extraction, and establish the new state-of-the-art on standard benchmarks (ACE04, ACE05 , and SciERC)  .
Revised:  In this work, we describe a very simple approach for joint entity and relation extraction, and establish the new state-of-the-art on standard benchmarks (ACE04, ACE05 , and SciERC) , obtaining a 1.7\%-2.8\% absolute improvement in relation F1 over previous joint models with the same pre-trained encoders .
Edit intent: meaning-changed
Original:  Finally, we also present an efficient approximation to our approach which requires only one pass of both  encoders at inference time, obtaining a 8-16\times speedup with a small accuracydrop .
Revised:  Finally, we also present an efficient approximation to our approach which requires only one pass of both entity and relation encoders at inference time, obtaining a 8-16\times speedup with a small accuracydrop .
Edit intent: meaning-changed
Original:  Finally, we also present an efficient approximation to our approach which requires only one pass of both  encoders at inference time, obtaining a 8-16\times speedup with a small accuracydrop .
Revised:  Finally, we also present an efficient approximation to our approach which requires only one pass of both encoders at inference time, achieving an 8-16\times speedup with a small accuracydrop .
Edit intent: fluency
Original:  Finally, we also present an efficient approximation to our approach which requires only one pass of both  encoders at inference time, obtaining a 8-16\times speedup with a small accuracydrop .
Revised:  Finally, we also present an efficient approximation to our approach which requires only one pass of both encoders at inference time, obtaining a 8-16\times speedup with a slight reduction in accuracy .
Edit intent: meaning-changed
Original:  As a form of toxic and abusive language, ad hominems contain harmful language that could further amplify the skew of power inequality for marginalized populations . Since dialogue systems are designed to respond directly to user input, it is important to study ad hominems in these system responses.
Revised:  These attacks are harmful because they propagate implicit biases and diminish a person's credibility . Since dialogue systems are designed to respond directly to user input, it is important to study ad hominems in these system responses.
Edit intent: meaning-changed
Original:  As a form of toxic and abusive language, ad hominems contain harmful language that could further amplify the skew of power inequality for marginalized populations . Since dialogue systems are designed to respond directly to user input, it is important to study ad hominems in these system responses.
Revised:  As a form of toxic and abusive language, ad hominems contain harmful language that could further amplify the skew of power inequality for marginalized populations . Since dialogue systems respond directly to user input, it is important to study ad hominems in these system responses.
Edit intent: clarity
Original:  As a form of toxic and abusive language, ad hominems contain harmful language that could further amplify the skew of power inequality for marginalized populations . Since dialogue systems are designed to respond directly to user input, it is important to study ad hominems in these system responses. In this work , we propose categories of ad hominems that allow us to analyze human and dialogue system responses to  Twitter posts.
Revised:  As a form of toxic and abusive language, ad hominems contain harmful language that could further amplify the skew of power inequality for marginalized populations . Since dialogue systems are designed to respond directly to user input, it is important to study ad hominems in dialogue responses. To this end , we propose categories of ad hominems that allow us to analyze human and dialogue system responses to Twitter posts.
Edit intent: clarity
Original:  We specifically compare responses to Twitter posts about marginalized communities (#BlackLivesMatter, #MeToo) and other topics (#Vegan, #WFH)  . Furthermore, we propose a constrained decoding technique that uses salient n-gram similarity to apply soft constraints to top-k sampling and can decrease the amount of ad hominems generated by dialogue systems .
Revised:  We specifically compare responses to Twitter topics about marginalized communities (#BlackLivesMatter, #MeToo) and other topics (#Vegan, #WFH) . Furthermore, we propose a constrained decoding technique that uses salient n-gram similarity to apply soft constraints to top-k sampling and can decrease the amount of ad hominems generated by dialogue systems .
Edit intent: clarity
Original:  We specifically compare responses to Twitter posts about marginalized communities (#BlackLivesMatter, #MeToo) and other topics (#Vegan, #WFH)  . Furthermore, we propose a constrained decoding technique that uses salient n-gram similarity to apply soft constraints to top-k sampling and can decrease the amount of ad hominems generated by dialogue systems .
Revised:  We specifically compare responses to Twitter posts about marginalized communities (#BlackLivesMatter, #MeToo) versus other topics (#Vegan, #WFH) . Furthermore, we propose a constrained decoding technique that uses salient n-gram similarity to apply soft constraints to top-k sampling and can decrease the amount of ad hominems generated by dialogue systems .
Edit intent: clarity
Original:  We specifically compare responses to Twitter posts about marginalized communities (#BlackLivesMatter, #MeToo) and other topics (#Vegan, #WFH)  . Furthermore, we propose a constrained decoding technique that uses salient n-gram similarity to apply soft constraints to top-k sampling and can decrease the amount of ad hominems generated by dialogue systems .
Revised:  We specifically compare responses to Twitter posts about marginalized communities (#BlackLivesMatter, #MeToo) and other topics (#Vegan, #WFH) , because the abusive language of ad hominems could further amplify the skew of power away from marginalized populations . Furthermore, we propose a constrained decoding technique that uses salient n-gram similarity to apply soft constraints to top-k sampling and can decrease the amount of ad hominems generated by dialogue systems .
Edit intent: meaning-changed
Original:  We specifically compare responses to Twitter posts about marginalized communities (#BlackLivesMatter, #MeToo) and other topics (#Vegan, #WFH)  . Furthermore, we propose a constrained decoding technique that uses salient n-gram similarity to apply soft constraints to top-k sampling and can decrease the amount of ad hominems generated by dialogue systems .
Revised:  We specifically compare responses to Twitter posts about marginalized communities (#BlackLivesMatter, #MeToo) and other topics (#Vegan, #WFH) . Furthermore, we propose a constrained decoding technique that uses salient n-gram similarity as a soft constraint for top-k sampling and can decrease the amount of ad hominems generated by dialogue systems .
Edit intent: fluency
Original:  We specifically compare responses to Twitter posts about marginalized communities (#BlackLivesMatter, #MeToo) and other topics (#Vegan, #WFH)  . Furthermore, we propose a constrained decoding technique that uses salient n-gram similarity to apply soft constraints to top-k sampling and can decrease the amount of ad hominems generated by dialogue systems .
Revised:  We specifically compare responses to Twitter posts about marginalized communities (#BlackLivesMatter, #MeToo) and other topics (#Vegan, #WFH) . Furthermore, we propose a constrained decoding technique that uses salient n-gram similarity to apply soft constraints to top-k sampling to reduce the amount of ad hominems generated by dialogue systems .
Edit intent: coherence
Original:  We specifically compare responses to Twitter posts about marginalized communities (#BlackLivesMatter, #MeToo) and other topics (#Vegan, #WFH)  . Furthermore, we propose a constrained decoding technique that uses salient n-gram similarity to apply soft constraints to top-k sampling and can decrease the amount of ad hominems generated by dialogue systems .
Revised:  We specifically compare responses to Twitter posts about marginalized communities (#BlackLivesMatter, #MeToo) and other topics (#Vegan, #WFH) . Furthermore, we propose a constrained decoding technique that uses salient n-gram similarity to apply soft constraints to top-k sampling and can decrease the amount of ad hominems generated .
Edit intent: clarity
Original:  Our results indicate that 1) responses composed by both humans and DialoGPT contain more ad hominems for discussions around marginalized communities versus other topics , 2) different amounts of ad hominems in the training data can influence the likelihood of the model generating ad hominems, and 3) we can thus carefully choose training data and use constrained decoding techniques to decrease the amount of ad hominems generated by dialogue systems .
Revised:  Our results indicate that 1) responses from both humans and DialoGPT contain more ad hominems for discussions around marginalized communities versus other topics , 2) different amounts of ad hominems in the training data can influence the likelihood of the model generating ad hominems, and 3) we can thus carefully choose training data and use constrained decoding techniques to decrease the amount of ad hominems generated by dialogue systems .
Edit intent: clarity
Original:  Our results indicate that 1) responses composed by both humans and DialoGPT contain more ad hominems for discussions around marginalized communities versus other topics , 2) different amounts of ad hominems in the training data can influence the likelihood of the model generating ad hominems, and 3) we can thus carefully choose training data and use constrained decoding techniques to decrease the amount of ad hominems generated by dialogue systems .
Revised:  Our results indicate that 1) responses composed by both humans and DialoGPT contain more ad hominems for discussions around marginalized communities , 2) different amounts of ad hominems in the training data can influence the likelihood of the model generating ad hominems, and 3) we can thus carefully choose training data and use constrained decoding techniques to decrease the amount of ad hominems generated by dialogue systems .
Edit intent: clarity
Original:  Our results indicate that 1) responses composed by both humans and DialoGPT contain more ad hominems for discussions around marginalized communities versus other topics , 2) different amounts of ad hominems in the training data can influence the likelihood of the model generating ad hominems, and 3) we can thus carefully choose training data and use constrained decoding techniques to decrease the amount of ad hominems generated by dialogue systems .
Revised:  Our results indicate that 1) responses composed by both humans and DialoGPT contain more ad hominems for discussions around marginalized communities versus other topics , 2) different quantities of ad hominems in the training data can influence the likelihood of the model generating ad hominems, and 3) we can thus carefully choose training data and use constrained decoding techniques to decrease the amount of ad hominems generated by dialogue systems .
Edit intent: clarity
Original:  Our results indicate that 1) responses composed by both humans and DialoGPT contain more ad hominems for discussions around marginalized communities versus other topics , 2) different amounts of ad hominems in the training data can influence the likelihood of the model generating ad hominems, and 3) we can thus carefully choose training data and use constrained decoding techniques to decrease the amount of ad hominems generated by dialogue systems .
Revised:  Our results indicate that 1) responses composed by both humans and DialoGPT contain more ad hominems for discussions around marginalized communities versus other topics , 2) different amounts of ad hominems in the training data can influence the likelihood of generating ad hominems, and 3) we can thus carefully choose training data and use constrained decoding techniques to decrease the amount of ad hominems generated by dialogue systems .
Edit intent: clarity
Original:  Our results indicate that 1) responses composed by both humans and DialoGPT contain more ad hominems for discussions around marginalized communities versus other topics , 2) different amounts of ad hominems in the training data can influence the likelihood of the model generating ad hominems, and 3) we can thus carefully choose training data and use constrained decoding techniques to decrease the amount of ad hominems generated by dialogue systems .
Revised:  Our results indicate that 1) responses composed by both humans and DialoGPT contain more ad hominems for discussions around marginalized communities versus other topics , 2) different amounts of ad hominems in the training data can influence the likelihood of the model generating ad hominems, and 3) we can use constrained decoding techniques to decrease the amount of ad hominems generated by dialogue systems .
Edit intent: coherence
Original:  Our results indicate that 1) responses composed by both humans and DialoGPT contain more ad hominems for discussions around marginalized communities versus other topics , 2) different amounts of ad hominems in the training data can influence the likelihood of the model generating ad hominems, and 3) we can thus carefully choose training data and use constrained decoding techniques to decrease the amount of ad hominems generated by dialogue systems .
Revised:  Our results indicate that 1) responses composed by both humans and DialoGPT contain more ad hominems for discussions around marginalized communities versus other topics , 2) different amounts of ad hominems in the training data can influence the likelihood of the model generating ad hominems, and 3) we can thus carefully choose training data and use constrained decoding techniques to reduce ad hominems in generated dialogue responses .
Edit intent: clarity
Original:  However, the paired text-image data required for pre-training are hard to collect and scale up. We investigate if a strong V&L representation model can be learned without text-image pairs.
Revised:  However, the paired text-image data required for pre-training are hard to collect and require cumbersome curation. Inspired by unsupervised machine translation, we investigate if a strong V&L representation model can be learned without text-image pairs.
Edit intent: meaning-changed
Original:  We investigate if a strong V&L representation model can be learned without text-image pairs. We propose Weakly-supervised VisualBERT with the key idea of conducting " mask-and-predict " pre-training on language-only and image-only corpora .
Revised:  We investigate if a strong V&L representation model can be learned through unsupervised pre-training without image-caption corpora. In particular, we propose to conduct `` mask-and-predict " pre-training on language-only and image-only corpora .
Edit intent: clarity