wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Given a   B\"uchi  automaton with n states and a finite degree of ambiguity, we show that the number of states in the complementary B\"uchi automaton constructed by the classical Rank-based and Slice-based complementation constructions can be improved, respectively, to 2^{ \mathcal{O (n)} from 2^{ \mathcal{O (n\log n)} and to \mathcal{O (4^n) from \mathcal{O ((3n)^n) , based on reduced run DAGs. To the best of our knowledge, the improved complexity is exponentially better than best known result of \mathcal{O 21%DIFDELCMD < ] %%% for complementing B\"uchi automata with a finite degree of ambiguity .
Revised:  Given a B\"uchi automaton with n states and a finite degree of ambiguity, we show that the number of states in the complementary B\"uchi automaton constructed by the classical Rank-based and Slice-based complementation constructions can be improved, respectively, to 2^{ \mathcal{O (n)} from 2^{ \mathcal{O (n\log n)} and to \mathcal{O (4^n) from \mathcal{O ((3n)^n) 21%DIFDELCMD < ] %%% for complementing B\"uchi automata with a finite degree of ambiguity .
Edit intent: meaning-changed
Original:  To the best of our knowledge, the improved complexity is exponentially better than best known result of \mathcal{O 21%DIFDELCMD < ] %%% for complementing B\"uchi automata with a finite degree of ambiguity .
Revised:  To the best of our knowledge, the improved complexity is exponentially better than best known result of \mathcal{O %DIFDELCMD < ] %%% for complementing B\"uchi automata with a finite degree of ambiguity .
Edit intent: meaning-changed
Original:  To the best of our knowledge, the improved complexity is exponentially better than best known result of \mathcal{O 21%DIFDELCMD < ] %%% for complementing B\"uchi automata with a finite degree of ambiguity .
Revised:  To the best of our knowledge, the improved complexity is exponentially better than best known result of \mathcal{O 21%DIFDELCMD < ] %%% .
Edit intent: coherence
Original:   We also conduct sensitivity analysis to assess the key instrumental variable assumptions, and carry out simulations to compare the proposed methods with common alternative methods .
Revised:  In particular, urban males suffer the most from being only children, and the negative effect has larger magnitude if the families were more resistant to the One-Child Policy. We also conduct sensitivity analysis to assess the key instrumental variable assumptions, and carry out simulations to compare the proposed methods with common alternative methods .
Edit intent: meaning-changed
Original: The similarity between a pair of time series, i.e., sequences of indexed values in time order, is often estimated by the dynamic time warping (DTW) distance, instead of any in the well-studied family of measurements including the longest common subsequence (LCS) length and the edit distance.
Revised: The similarity between a pair of time series, i.e., sequences of indexed values in time order, is often estimated by the dynamic time warping (DTW) distance, instead of any in the well-studied family of measures including the longest common subsequence (LCS) length and the edit distance.
Edit intent: fluency
Original:  Although it may seem as if the DTW and  LCS(-like) measurements are essentially different, we reveal that the DTW distance can be represented by the longest increasing subsequence (LIS) length of a sequence of integers, which is the LCS length between the integer sequence and itself sorted.
Revised:  Although it may seem as if the DTW and the LCS(-like) measurements are essentially different, we reveal that the DTW distance can be represented by the longest increasing subsequence (LIS) length of a sequence of integers, which is the LCS length between the integer sequence and itself sorted.
Edit intent: fluency
Original:  Although it may seem as if the DTW and  LCS(-like) measurements are essentially different, we reveal that the DTW distance can be represented by the longest increasing subsequence (LIS) length of a sequence of integers, which is the LCS length between the integer sequence and itself sorted.
Revised:  Although it may seem as if the DTW and LCS(-like) measures are essentially different, we reveal that the DTW distance can be represented by the longest increasing subsequence (LIS) length of a sequence of integers, which is the LCS length between the integer sequence and itself sorted.
Edit intent: clarity
Original:   To demonstrate that techniques developed under  LCS(-like) measurements are directly applicable to analysis of time series via our reduction of DTW to LIS, we present time-efficient algorithms for DTW-related problems utilizing the semi-local sequence comparison technique developed for LCS-related problems.
Revised:  For a given pair of time series of length n such that the dissimilarity between any elements is an integer between zero and c, we propose an integer sequence that represents any substring-substring DTW distance as its band-substring LIS length. The length of the produced integer sequence is O(c n^2), which can be translated to O(n^2) for constant dissimilarity functions. To demonstrate that techniques developed under LCS(-like) measurements are directly applicable to analysis of time series via our reduction of DTW to LIS, we present time-efficient algorithms for DTW-related problems utilizing the semi-local sequence comparison technique developed for LCS-related problems.
Edit intent: meaning-changed
Original:   To demonstrate that techniques developed under  LCS(-like) measurements are directly applicable to analysis of time series via our reduction of DTW to LIS, we present time-efficient algorithms for DTW-related problems utilizing the semi-local sequence comparison technique developed for LCS-related problems.
Revised:  To demonstrate that techniques developed under the LCS(-like) measurements are directly applicable to analysis of time series via our reduction of DTW to LIS, we present time-efficient algorithms for DTW-related problems utilizing the semi-local sequence comparison technique developed for LCS-related problems.
Edit intent: fluency
Original:   To demonstrate that techniques developed under  LCS(-like) measurements are directly applicable to analysis of time series via our reduction of DTW to LIS, we present time-efficient algorithms for DTW-related problems utilizing the semi-local sequence comparison technique developed for LCS-related problems.
Revised:  To demonstrate that techniques developed under LCS(-like) measures are directly applicable to analysis of time series via our reduction of DTW to LIS, we present time-efficient algorithms for DTW-related problems utilizing the semi-local sequence comparison technique developed for LCS-related problems.
Edit intent: clarity
Original:  These tweets on social media play an important role in sharing information, express opinion, seek support and help during the epidemic. To facilitate the social media studies of COVID-19 and enable better and faster researches to combat the spread of this disease , we release Weibo-COV, a first large-scale COVID-19 related tweets dataset from Weibo, covering more than 30 million tweets from 1 November 2019 t0 30 April 2020.
Revised:  These tweets on social media play an important role in sharing information, express opinion, seek support and help during the pandemic, and these tweets on social media are valuable for studies against COVID-19 and enable better and faster researches to combat the spread of this disease , we release Weibo-COV, a first large-scale COVID-19 related tweets dataset from Weibo, covering more than 30 million tweets from 1 November 2019 t0 30 April 2020.
Edit intent: coherence
Original:  To facilitate the social media studies of COVID-19 and enable better and faster researches to combat the spread of this disease , we release Weibo-COV, a first large-scale COVID-19 related tweets dataset from Weibo, covering more than 30 million tweets from 1 November 2019 t0 30 April 2020.
Revised:  To facilitate the social media studies of COVID-19 , such as disease surveillance. Therefore, in this paper, we release Weibo-COV, a first large-scale COVID-19 related tweets dataset from Weibo, covering more than 30 million tweets from 1 November 2019 t0 30 April 2020.
Edit intent: meaning-changed
Original:  To facilitate the social media studies of COVID-19 and enable better and faster researches to combat the spread of this disease , we release Weibo-COV, a first large-scale COVID-19 related tweets dataset from Weibo, covering more than 30 million tweets from 1 November 2019 t0 30 April 2020.
Revised:  To facilitate the social media studies of COVID-19 and enable better and faster researches to combat the spread of this disease , we release Weibo-COV, a first large-scale COVID-19 social media dataset from Weibo, covering more than 30 million tweets from 1 November 2019 t0 30 April 2020.
Edit intent: meaning-changed
Original:  To facilitate the social media studies of COVID-19 and enable better and faster researches to combat the spread of this disease , we release Weibo-COV, a first large-scale COVID-19 related tweets dataset from Weibo, covering more than 30 million tweets from 1 November 2019 t0 30 April 2020.
Revised:  To facilitate the social media studies of COVID-19 and enable better and faster researches to combat the spread of this disease , we release Weibo-COV, a first large-scale COVID-19 related tweets dataset from Weibo, covering more than 30 million tweets from 1 November 2019 to 30 April 2020.
Edit intent: fluency
Original:  Moreover, the fields of tweets in the dataset is very rich, including basic tweets information, interactive information, location information and retweet network  .
Revised:  Moreover, the fields of tweets in the dataset is very rich, including basic tweets information, interactive information, location information and retweet network . We hope this dataset can promote studies of COVID-19 from multiple perspectives and enable better and faster researches to combat the spread of this disease .
Edit intent: meaning-changed
Original:  Therefore, in this paper, we release Weibo-COV, a first large-scale COVID-19 social media dataset from Weibo  , covering more than 30 million tweets from 1 November 2019 to 30 April 2020.
Revised:  Therefore, in this paper, we release a large-scale COVID-19 social media dataset from Weibo , covering more than 30 million tweets from 1 November 2019 to 30 April 2020.
Edit intent: clarity
Original:  Therefore, in this paper, we release Weibo-COV, a first large-scale COVID-19 social media dataset from Weibo  , covering more than 30 million tweets from 1 November 2019 to 30 April 2020.
Revised:  Therefore, in this paper, we release Weibo-COV, a first large-scale COVID-19 social media dataset from Weibo called Weibo-COV , covering more than 30 million tweets from 1 November 2019 to 30 April 2020.
Edit intent: meaning-changed
Original:  Therefore, in this paper, we release a  large-scale COVID-19 social media dataset from Weibo called Weibo-COV, covering more than 30 million tweets from 1 November 2019 to 30 April 2020.
Revised:  Therefore, in this paper, we release a novel large-scale COVID-19 social media dataset from Weibo called Weibo-COV, covering more than 30 million tweets from 1 November 2019 to 30 April 2020.
Edit intent: meaning-changed
Original:  Therefore, in this paper, we release a  large-scale COVID-19 social media dataset from Weibo called Weibo-COV, covering more than 30 million tweets from 1 November 2019 to 30 April 2020.
Revised:  Therefore, in this paper, we release a large-scale COVID-19 social media dataset from Weibo called Weibo-COV, covering more than 40 million tweets from 1 November 2019 to 30 April 2020.
Edit intent: meaning-changed
Original:  Therefore, in this paper, we release a  large-scale COVID-19 social media dataset from Weibo called Weibo-COV, covering more than 30 million tweets from 1 November 2019 to 30 April 2020.
Revised:  Therefore, in this paper, we release a large-scale COVID-19 social media dataset from Weibo called Weibo-COV, covering more than 30 million tweets from 1 December 2019 to 30 April 2020.
Edit intent: meaning-changed
Original:  Therefore, in this paper, we release a novel  large-scale COVID-19 social media dataset from Weibo called Weibo-COV, covering more than 40 million tweets from 1 December 2019 to 30 April 2020.
Revised:  Therefore, in this paper, we release a novel and fine-grained large-scale COVID-19 social media dataset from Weibo called Weibo-COV, covering more than 40 million tweets from 1 December 2019 to 30 April 2020.
Edit intent: meaning-changed
Original:  Therefore, in this paper, we release a novel  large-scale COVID-19 social media dataset from Weibo called Weibo-COV, covering more than 40 million tweets from 1 December 2019 to 30 April 2020.
Revised:  Therefore, in this paper, we release a novel large-scale COVID-19 social media dataset collected from Sina Weibo, named Weibo-COV, covering more than 40 million tweets from 1 December 2019 to 30 April 2020.
Edit intent: meaning-changed
Original:  Therefore, in this paper, we release a novel  large-scale COVID-19 social media dataset from Weibo called Weibo-COV, covering more than 40 million tweets from 1 December 2019 to 30 April 2020.
Revised:  Therefore, in this paper, we release a novel large-scale COVID-19 social media dataset from Weibo called Weibo-COV, contains more than 40 million tweets from 1 December 2019 to 30 April 2020.
Edit intent: clarity
Original:  Therefore, in this paper, we release a novel  large-scale COVID-19 social media dataset from Weibo called Weibo-COV, covering more than 40 million tweets from 1 December 2019 to 30 April 2020.
Revised:  Therefore, in this paper, we release a novel large-scale COVID-19 social media dataset from Weibo called Weibo-COV, covering more than 40 million posts ranging from December 1, 2019 to 30 April 2020.
Edit intent: meaning-changed
Original:  Therefore, in this paper, we release a novel  large-scale COVID-19 social media dataset from Weibo called Weibo-COV, covering more than 40 million tweets from 1 December 2019 to 30 April 2020.
Revised:  Therefore, in this paper, we release a novel large-scale COVID-19 social media dataset from Weibo called Weibo-COV, covering more than 40 million tweets from 1 December 2019 to April 30, 2020.
Edit intent: clarity
Original:  This limit cycle is nothing but the time eliminated Lissajous plot of the concentrations of Adenosine-diphosphate (ADP) and fructose-6-phosphate (F6P) of a normal/healthy human.
Revised:  This limit cycle is nothing but the time eliminated Lissajous plot of the concentrations of Adenosine-diphosphate (ADP) and Fructose-6-phosphate (F6P) of a normal/healthy human.
Edit intent: fluency
Original: This paper deals with cross-lingual transfer learning for dialogue act (DA) recognition. Besides generic contextual information gathered from pre-trained BERT embeddings, our objective is to transfer models trained on a standard English DA corpus to two other languages, German and French, and to potentially very different types of dialogue with different dialogue acts than the standard well-known DA corpora. The proposed approach thus studies the applicability of automatic DA recognition to specific tasks that may not benefit from a large enough number of manual annotations. A key component of our architecture is the automatic translation module, which limitations are addressed by stacking both foreign and translated words sequences into the same model. We further compare both CNN and multi-head self-attention to compute the speaker turn embeddings and show that in low-resource situations, the best results are obtained by combining all sources of transferred information  .
Revised: This paper deals with cross-lingual models to enable dialogue act recognition for specific tasks with a small number of annotations. We design a transfer learning approach for dialogue act recognition and validate it on two different target languages and domains. We compute dialogue turn embeddings with both a CNN and multi-head self-attention to compute the speaker turn embeddings and show that in low-resource situations, the best results are obtained by combining all sources of transferred information .
Edit intent: clarity
Original:  We further compare both CNN and multi-head self-attention to compute the speaker turn embeddings and show that in low-resource situations, the best results are obtained by combining all sources of transferred information  .
Revised:  We further compare both CNN and multi-head self-attention to compute the speaker turn embeddings and show that in low-resource situations, the best results are obtained by combining all sources of transferred information . We further demonstrate that the proposed methods significantly outperform related cross-lingual DA recognition approaches .
Edit intent: meaning-changed
Original: Common end-to-end models like CTC or encoder-decoder-attention models use characters or subword units like BPEas the output labels. We do systematic comparisons between grapheme-based and phoneme-based output labels .
Revised: Common end-to-end models like CTC or encoder-decoder-attention models for automatic speech recognition (ASR) use graphemes or grapheme-based subword units based on e.g. byte-pair encoding (BPE). The mapping from pronunciation to spelling is learned completely from data. In contrast to this, classical approaches to ASR employ secondary knowledge sources in the form of phoneme lists to define phonetic output labels and pronunciation lexica. In this work, we do a systematic comparison between grapheme- and phoneme-based output labels .
Edit intent: meaning-changed
Original:  We do systematic comparisons between grapheme-based and phoneme-based output labels . These can be single phonemes without context (~40 labels), or multiple phonemes together in one output label, such that we get phoneme-based subwords. For this purpose, we introduce phoneme-based BPE labels . In further experiments, we extend the phoneme set by auxiliary units to be able to discriminate homophones(different words with same pronunciation).
Revised:  We do systematic comparisons between grapheme-based and phoneme-based output labels for an encoder-decoder-attention ASR model. We investigate the use of single phonemes as well as BPE-based phoneme groups as output labels of our model. To preserve a simplified and efficient decoder design, we also extend the phoneme set by auxiliary units to be able to discriminate homophones(different words with same pronunciation).
Edit intent: clarity
Original:  In further experiments, we extend the phoneme set by auxiliary units to be able to discriminate homophones(different words with same pronunciation). This enables a very simple and efficient decoding algorithm. We perform the experiments on Switchboard 300h and we can show that our phoneme-based models are competitive to the grapheme-based models .
Revised:  In further experiments, we extend the phoneme set by auxiliary units to be able to distinguish homophones. Experiments performed on the Switchboard 300h and we can show that our phoneme-based models are competitive to the grapheme-based models .
Edit intent: coherence
Original: In discrete exchange economies with possibly redundant and joint ownership, the conventional strong core may be empty , while the weak core may include unintuitive outcomes. We propose new core notions in the conventional flavor by regarding endowments as rights to consume or trade with others. Our key idea is to identify self-enforcing coalitions and to redistribute their redundant property rights. Our first notion lies between the strong core and the weak core and is independent of Balbuzanov and Kotowski's (2019) exclusion core .
Revised: In discrete exchange economies with possibly redundant and joint ownership, the conventional strong core may be empty . This motivates Balbuzanov and Kotowski (2019) to interpret endowments as exclusion rights and propose a new concept called exclusion core. Our contribution is twofold. First, we propose a rectification of the core to solve its problem under complex endowments. Second, we propose a refinement of Balbuzanov and Kotowski's (2019) exclusion core .
Edit intent: clarity
Original:  Our first notion lies between the strong core and the weak core and is independent of Balbuzanov and Kotowski's (2019) exclusion core . Our second notion refines the first and the exclusion core by combining their different merits. We generalize the You Request My House - I Get Your Turn mechanism to find our core allocations .
Revised:  Our first notion lies between the strong core and the weak core and is independent of Balbuzanov and Kotowski's exclusion core to improve its performance. Our two core concepts share a common idea of correcting the misused altruism of unaffected agents in blocking coalitions. We propose a mechanism to find our core allocations .
Edit intent: meaning-changed
Original:  Here, we develop a method that classifies the source of global fluctuations of temporal networks according to these two mechanisms.We propose a dynamic hidden-variable model to formally define the two dynamical classes , with which we show that the global fluctuations in a real-world dynamical system can be explained by either of the two simple mechanisms.
Revised:  Here, we develop a method that classifies the source of global fluctuations of temporal networks . In doing so, we first show empirical evidence that real-world dynamical systems could be categorized into two classes, the difference of which is characterized by the way the number of edges grows with the number of active nodes, i.e., densification scaling. We develop a dynamic hidden-variable model to formally define the two dynamical classes , with which we show that the global fluctuations in a real-world dynamical system can be explained by either of the two simple mechanisms.
Edit intent: meaning-changed
Original: We propose a dynamic hidden-variable model to formally define the two dynamical classes , with which we show that the global fluctuations in a real-world dynamical system can be explained by either of the two simple mechanisms.
Revised: We propose a dynamic hidden-variable model to formally characterize the two dynamical classes , with which we show that the global fluctuations in a real-world dynamical system can be explained by either of the two simple mechanisms.
Edit intent: clarity
Original: We propose a dynamic hidden-variable model to formally define the two dynamical classes , with which we show that the global fluctuations in a real-world dynamical system can be explained by either of the two simple mechanisms. Our findings will contribute to a better understanding of the origin of the time-varying nature of complex networks .
Revised: We propose a dynamic hidden-variable model to formally define the two dynamical classes . The model is fitted to the time-varying nature of complex networks .
Edit intent: clarity
Original:  It has long been thought that human sentence reading involves something akin to recurrence and so RNNs may still have an advantage over the Transformer as a cognitive model. In this paper we train both Transformer and RNN based language modelsand compare their performance as a model of human sentence processing. We use the trained language models to compute surprisal values for the stimuli used in several reading experiments and use mixed linear modelling to measure how well the surprisal explains measures of human reading effort.
Revised:  We compare Transformer- and RNN-based language models' ability to account for measures of human reading effort.
Edit intent: clarity
Original:  Our analysis shows that the Transformers outperform the RNNs as cognitive models in explaining self-paced reading times and N400 strength but not gaze durations from an eye-tracking experiment .
Revised:  Our analysis shows that the Transformers outperform the RNNs as cognitive models in explaining self-paced reading times and neural activity during reading English sentences, challenging the widely held idea that human sentence processing involves recurrent and immediate processing and provides evidence for cue-based retrieval .
Edit intent: meaning-changed