wanyu/IteraTeR_full_sent huggingface dataset:
Original:  The choice of factorized vs. unfactorized architectures and a multi-task training setup affect parsing accuracy in more subtle ways, depending on target language and output representation (trees vs. graphs). Our parser achieves new state-of-the-art results for a wide range of languages on both basic as well as enhanced Universal Dependencies, using a unified and comparatively simple architecture for both parsing tasks .
Revised:  The choice of factorized vs. unfactorized architectures and a multi-task training setup outputting additional UD features may contort results. Taking these insights together, we propose a simple but widely applicable parser architecture and configuration, achieving new state-of-the-art results for a wide range of languages on both basic as well as enhanced Universal Dependencies, using a unified and comparatively simple architecture for both parsing tasks .
Edit intent: clarity
Original: Linguists characterize dialects by the presence, absence, and frequency of dozens of interpretable features . Detecting these features in text has applications to social science and dialectology, and can be used to assess the robustness of natural language processing systems to dialect differences  .
Revised: Linguists characterize dialects by the presence, absence, and frequency of dozens of dialect features in speech and text, such as the deletion of the copula in "He .
Edit intent: meaning-changed
Original:  Detecting these features in text has applications to social science and dialectology, and can be used to assess the robustness of natural language processing systems to dialect differences  .
Revised:  Detecting these features in text has applications to social science and dialectology, and can be used to assess the robustness of natural language processing systems to dialect differences running". In this paper, we introduce the task of dialect feature detection, and present two multitask learning approaches, both based on pretrained transformers .
Edit intent: meaning-changed
Original:  Linguists typically define dialect features by providing a small number of minimal pairs, which are paired examples distinguished only by whether the feature is present, while holding everything else constant. In this paper, we present two multitask learning architectures for recognizing dialect features, both based on pretrained transformers. We evaluate these models on two test sets of Indian English, annotated for a total of 22 dialect features .
Revised:  Linguists typically define dialect features by providing a small number of minimal pairs, building on how linguists typically define dialect features. Evaluation on a test set of 22 dialect features .
Edit intent: clarity
Original:  On the other hand, pure character-level models, though robust to misspellings,  often lead to unreasonably large sequence lengths and make it harder for the model to learn meaningful contiguous characters.
Revised:  On the other hand, pure character-level models, though robust to misspellings, pure character-level models often lead to unreasonably large sequence lengths and make it harder for the model to learn meaningful contiguous characters.
Edit intent: meaning-changed
Original:  To alleviate these challenges, we propose a character-based subword transformer module (char2subword) that learns the subword embedding table in pre-trained models like BERT  . Our char2subword module builds representations from characters out of the subword vocabulary, and it can be used as a drop-in replacement of the subword embedding table.
Revised:  We propose a character-based subword transformer module (char2subword) that learns the subword embedding table in pre-trained models like BERT . Our char2subword module builds representations from characters out of the subword vocabulary, and it can be used as a drop-in replacement of the subword embedding table.
Edit intent: coherence
Original:  To alleviate these challenges, we propose a character-based subword transformer module (char2subword) that learns the subword embedding table in pre-trained models like BERT  . Our char2subword module builds representations from characters out of the subword vocabulary, and it can be used as a drop-in replacement of the subword embedding table.
Revised:  To alleviate these challenges, we propose a character-based subword module (char2subword) that learns the subword embedding table in pre-trained models like BERT . Our char2subword module builds representations from characters out of the subword vocabulary, and it can be used as a drop-in replacement of the subword embedding table.
Edit intent: clarity
Original:  To alleviate these challenges, we propose a character-based subword transformer module (char2subword) that learns the subword embedding table in pre-trained models like BERT  . Our char2subword module builds representations from characters out of the subword vocabulary, and it can be used as a drop-in replacement of the subword embedding table.
Revised:  To alleviate these challenges, we propose a character-based subword transformer module (char2subword) that learns the subword embedding table in pre-trained models like BERT to alleviate these challenges . Our char2subword module builds representations from characters out of the subword vocabulary, and it can be used as a drop-in replacement of the subword embedding table.
Edit intent: coherence
Original: Existing works on temporal reasoning among events described in text focus on modeling relationships between explicitly mentioned events and do not handle event end time effectively. However, human readers can infer from natural language text many implicit events that help them better understand the situation and, consequently, better reason about time. This work proposes a new crowd-sourced dataset, TRACIE, which evaluates systems ' understanding of implicit events - events that are not mentioned explicitly in the text but can be inferred from it.
Revised: We propose TRACIE, a novel temporal reasoning dataset that evaluates the degree to which systems understand implicit events -- events that are not mentioned explicitly in the text but can be inferred from it.
Edit intent: clarity
Original:  This work proposes a new crowd-sourced dataset, TRACIE, which evaluates systems ' understanding of implicit events - events that are not mentioned explicitly in the text but can be inferred from it.
Revised:  This work proposes a new crowd-sourced dataset, TRACIE, which evaluates systems ' understanding of implicit events - events that are not mentioned explicitly in natural language text but can be inferred from it.
Edit intent: meaning-changed
Original:  This is done via textual entailment instances querying both start and end times of events. We show that TRACIE is challenging for state-of-the-art language models .
Revised:  This introduces a new challenge in temporal reasoning research, where prior work has focused on explicitly mentioned events. Human readers can infer implicit events via commonsense reasoning, resulting in a more comprehensive understanding of the situation and, consequently, better reasoning about time. We find, however, that state-of-the-art language models .
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Original:  We show that TRACIE is challenging for state-of-the-art language models . Our proposed model, SymTime, exploits distant supervision signals from the text itself and reasons over events' start time and duration to infer events' end time points.
Revised:  We show that TRACIE is challenging for state-of-the-art models struggle when predicting temporal relationships between implicit and explicit events. To address this, we propose a neuro-symbolic temporal reasoning model, SYMTIME, which exploits distant supervision signals from the text itself and reasons over events' start time and duration to infer events' end time points.
Edit intent: meaning-changed
Original:  Our proposed model, SymTime, exploits distant supervision signals from the text itself and reasons over events' start time and duration to infer events' end time points. We show that our approach improves over baseline language models, gaining 5\% on the i.i.d. split and 9\% on an out-of-distribution test split .
Revised:  Our proposed model, SymTime, exploits distant supervision signals from large-scale text and uses temporal rules to combine start times and durations to infer end times. SYMTIME outperforms strong baseline systems on TRACIE by 5\% on the i.i.d. split and 9\% on an out-of-distribution test split .
Edit intent: clarity
Original: The existing dialogue corpora and models are typically designed under two disjoint motives: while task-oriented systems focus on achieving functional goals (e.g., booking hotels), open-domain chatbots aim at making socially engaging conversations.
Revised: Existing dialogue corpora and models are typically designed under two disjoint motives: while task-oriented systems focus on achieving functional goals (e.g., booking hotels), open-domain chatbots aim at making socially engaging conversations.
Edit intent: fluency
Original:  In this work, we propose to integrate both types of systems by Adding Chit-Chats to ENhance Task-ORiented dialogues (ACCENTOR), with the goal of making virtual assistant conversations more engaging and interactive.
Revised:  In this work, we propose to integrate both types of systems by Adding Chit-Chat to ENhance Task-ORiented dialogues (ACCENTOR), with the goal of making virtual assistant conversations more engaging and interactive.
Edit intent: fluency
Original:  We then present our new chit-chat annotations to 23.8K dialogues from the popular task-oriented datasets (Schema-Guided Dialogue and MultiWOZ 2.1) and demonstrate their advantage over the originals via human evaluation.
Revised:  We then present our new chit-chat-based annotations to 23.8K dialogues from the popular task-oriented datasets (Schema-Guided Dialogue and MultiWOZ 2.1) and demonstrate their advantage over the originals via human evaluation.
Edit intent: clarity
Original:  We then present our new chit-chat annotations to 23.8K dialogues from the popular task-oriented datasets (Schema-Guided Dialogue and MultiWOZ 2.1) and demonstrate their advantage over the originals via human evaluation.
Revised:  We then present our new chit-chat annotations to 23.8K dialogues from two popular task-oriented datasets (Schema-Guided Dialogue and MultiWOZ 2.1) and demonstrate their advantage over the originals via human evaluation.
Edit intent: clarity
Original:  We propose a modular architecture for following natural language instructions that describe sequences of diverse subgoals , such as navigating to landmarks or picking up objects.
Revised: Standard architectures used in instruction following often struggle on novel compositions of subgoals (e.g. navigating to landmarks or picking up objects) observed during training. We propose a modular architecture for following natural language instructions that describe sequences of diverse subgoals , such as navigating to landmarks or picking up objects.
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Original:  When compared to standard  sequence-to-sequence approaches on ALFRED, a challenging instruction following benchmark, we find that modularization improves generalization to  environments unseen in training and to novel tasks .
Revised:  When compared to standard , non-modular sequence-to-sequence approaches on ALFRED, a challenging instruction following benchmark, we find that modularization improves generalization to environments unseen in training and to novel tasks .
Edit intent: meaning-changed
Original:  When compared to standard  sequence-to-sequence approaches on ALFRED, a challenging instruction following benchmark, we find that modularization improves generalization to  environments unseen in training and to novel tasks .
Revised:  When compared to standard sequence-to-sequence approaches on ALFRED, a challenging instruction following benchmark, we find that modularization improves generalization to novel subgoal compositions, as well as to environments unseen in training and to novel tasks .
Edit intent: meaning-changed
Original:  Based on the maxim of Quantity (be informative), we propose Relative Utterance Quantity (RUQ) to diagnose the `I don't know' problem . The RUQ diagnostic compares the model score of a generic response to that of the reference response.
Revised:  Based on the maxim of Quantity (be informative), we propose Relative Utterance Quantity (RUQ) to diagnose the `I don't know' problem , in which a dialog system produces generic responses. The linguistically motivated RUQ diagnostic compares the model score of a generic response to that of the reference response.
Edit intent: meaning-changed
Original:  We find that for reasonable baseline models, `I don't know' is preferred over the reference more than half the time, but this can be mitigated with hyperparameter tuning  .
Revised:  We find that for reasonable baseline models, `I don't know' is preferred over the reference more than half the time, but this can be mitigated with hyperparameter tuning . RUQ allows for the direct analysis of the `I don't know' problem, which has been addressed but not analyzed by prior work .
Edit intent: meaning-changed
Original:  Existing works on event extraction generally confine on extracting events from single sentences, which fail to capture the relationships between the event mentions at the scale of a document, as well as the event arguments that appear in a different sentence than the event trigger.
Revised:  Existing works on event extraction usually confine on extracting events from single sentences, which fail to capture the relationships between the event mentions at the scale of a document, as well as the event arguments that appear in a different sentence than the event trigger.
Edit intent: clarity
Original: Natural language is one of the ways information is encoded and it has highly abstracted and conceptualized the information.
Revised: First of all, please URLet all you knew about the lexical classification, then let's jump to the conclusion. This paper reclassified lexical chunks into data chunks, structure chunks, and pointer chunks. Almost all data chunks are information sets. According to the difference of the set structures, data chunks can be further divided into attribute chunks and entity chunks. According to the different abstraction level and method, attribute chunks can be further divided into basic attribute chunks, extended attribute chunks, and advanced attribute chunks. All of the above classification principles are structural and functionalbased discrimination, instead of artificially divide lexical chunks into a noun, adjective, pronouns, and so on. Now, let's back to the normal study process. The author believes natural language is one of the ways information is encoded and it has highly abstracted and conceptualized the information.
Edit intent: meaning-changed
Original:  analyzes the classification coding system of attribute information  and the abstraction relation between attribute information and entities in the real world , constructs the storage model of information, and simulate the attribute information precessing process in one of the attribute spaces, interprets how the relations which represented by "Be", "Of", "Have", and so on are embodied in the information storage data structures and the corresponding data reading modes , reclassifies the sentences types from the perspective of task types and data reading modes.
Revised:  analyzes the classification coding system of attribute information and the abstraction relation between attribute information and entities in the real world . To have a clear and better discussion, the attribute spaces, interprets how the relations which represented by "Be", "Of", "Have", and so on are embodied in the information storage data structures and the corresponding data reading modes , reclassifies the sentences types from the perspective of task types and data reading modes.
Edit intent: clarity
Original:  constructs the storage model of information, and simulate the attribute information precessing process in one of the attribute spaces, interprets how the relations which represented by "Be", "Of", "Have", and so on are embodied in the information storage data structures and the corresponding data reading modes , reclassifies the sentences types from the perspective of task types and data reading modes.
Revised:  constructs the storage model of information, and simulate the attribute information precessing process in one of the author constructed corresponding data storage models, and extract three kinds of data reading modes on those data storage models, they are the defining reading mode which is driven by the structural word: be, the set reading mode which is driven by the structural word: have, and the process reading mode which is driven by verbs. Sentences output by the above data reading modes , reclassifies the sentences types from the perspective of task types and data reading modes.
Edit intent: meaning-changed
Original:  constructs the storage model of information, and simulate the attribute information precessing process in one of the attribute spaces, interprets how the relations which represented by "Be", "Of", "Have", and so on are embodied in the information storage data structures and the corresponding data reading modes , reclassifies the sentences types from the perspective of task types and data reading modes. Then, simulated the understanding process (the information processing process) on a dialogue example. Finally, the author summarizes the basic conditions of understanding and gives out the definition of understanding from a personal point of view. The study in this paper provides a practical, theoretical basis and research methods for NLU.It also can be applied in large-scale, multi-type information processing in the artificial intelligence (AI) area .
Revised:  constructs the storage model of information, and simulate the attribute information precessing process in one of the attribute spaces, interprets how the relations which represented by "Be", "Of", "Have", and so on are embodied in the information storage data structures and the corresponding data reading modes can be further divided into the data description task, the data verification task, and the data search task, according to task types represented by these sentences .. .
Edit intent: clarity
Original: First of all, please URLet all you knew about the lexical classification, then let's jump to the conclusion. This paper reclassified lexical chunks into data chunks, structure chunks, and pointer chunks.
Revised: We must recognize that natural language is a way of information encoding, and it encodes not only the information but also the procedures for how information is processed. To understand natural language, the same as we conceive and design computer languages, the first step is to separate information (or data) and the processing procedures of information (or data). In natural language, some processing procedures of data are encoded directly as the structure chunk and the pointer chunk (this paper has reclassified lexical chunks into data chunks, structure chunks, and pointer chunks.
Edit intent: meaning-changed
Original:  This paper reclassified lexical chunks into data chunks, structure chunks, and pointer chunks. Almost all data chunks are information sets.
Revised:  This paper reclassified lexical chunks as the data chunk, structure chunk, and the pointer chunk); some processing procedures of data imply in sentences structures; some requests of processing procedures are expressed by information senders and processed by information receivers. For the data parts, the classification encoding system of attribute information and the URLanization architecture (including constitutional structures of information sets.
Edit intent: meaning-changed
Original:  Almost all data chunks are information sets. According to the difference of the set structures, data chunks can be further divided into attribute chunks and entity chunks. According to the different abstraction level and method, attribute chunks can be further divided into basic attribute chunks, extended attribute chunks, and advanced attribute chunks. All of the above classification principles are structural and functionalbased discrimination, instead of artificially divide lexical chunks into a noun, adjective, pronouns, and so on. Now, let's back to the normal study process. The author believes natural language is one of the ways information is encoded and it has highly abstracted and conceptualized the information .
Revised:  Almost all data chunks are information sets and the hierarchy between the information sets) were discussed. In section 2, the theoretical part elaborated in section 2 has been verified in examples and proofed that the studies in this paper have achieved the goal of enabling machines to understand the information .
Edit intent: meaning-changed
Original:  The author believes natural language is one of the ways information is encoded and it has highly abstracted and conceptualized the information . Therefore the study begins with disassembling the information represented by natural language and then discovered the classification coding system of attribute information, and the abstraction relations between attribute information and entities in the real world. To have a clear and better discussion , the author constructed corresponding data storage models, and extract three kinds of data reading modes on those data storage models, they are the defining reading mode which is driven by the structural word: be, the set reading mode which is driven by the structural word: have, and the process reading mode which is driven by verbs.
Revised:  The author believes natural language is one of the ways information is encoded and it has highly abstracted and conceptualized the information conveyed in the dialogue. In section 4 , the author constructed corresponding data storage models, and extract three kinds of data reading modes on those data storage models, they are the defining reading mode which is driven by the structural word: be, the set reading mode which is driven by the structural word: have, and the process reading mode which is driven by verbs.
Edit intent: meaning-changed
Original:  To have a clear and better discussion , the author constructed corresponding data storage models, and extract three kinds of data reading modes on those data storage models, they are the defining reading mode which is driven by the structural word: be, the set reading mode which is driven by the structural word: have, and the process reading mode which is driven by verbs. Sentences output by the above data reading modes can be further divided into the data description task, the data verification task, and the data search task, according to task types represented by these sentences .. .
Revised:  To have a clear and better discussion , the author summarizes the basic conditions of "Understanding", rethinks what "Understanding" is and how to proceed. The study in this paper provides a practical, theoretical basis and research methods for NLU. It also can be applied in large-scale and multi-type information processing in the artificial intelligence (AI) area .
Edit intent: meaning-changed
Original: We present a method for inducing taxonomic trees from pretrained transformers. Given a set of input terms, we assign a score for the likelihood that each pair of terms forms a parent-child relation .
Revised: We present a method for constructing taxonomic trees (e.g., WordNet) using pretrained language models. Our approach is composed of two modules, one that predicts parenthood relations and another that reconciles those predictions into trees. The parenthood prediction module produces likelihood scores for each potential parent-child pair, creating a graph of parent-child relation .
Edit intent: meaning-changed