wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Motivated by boundaries of patterning fields related to  emergence of spatial form in embryonic development, we propose a set of mixed boundary conditions for a two-species reaction-diffusion system which forms inhomogeneous solutions away from the boundary of the domain for a variety of different reaction kinetics, with a prescribed uniform state near the boundary.
Revised:  Motivated by boundaries of patterning fields related to the emergence of spatial form in embryonic development, we propose a set of mixed boundary conditions for a two-species reaction-diffusion system which forms inhomogeneous solutions away from the boundary of the domain for a variety of different reaction kinetics, with a prescribed uniform state near the boundary.
Edit intent: fluency
Original:  In this paper, we propose a novel KnOwledge-Aware proceduraL text understAnding (KOALA) model, which leverages external knowledge sources to solve these issues . Specifically, we retrieve informative knowledge triples from ConceptNet and perform knowledge-aware reasoning while tracking the entities.
Revised:  In this paper, we propose a novel KnOwledge-Aware proceduraL text understAnding (KOALA) model, which effectively leverages multiple forms of external knowledge in this task . Specifically, we retrieve informative knowledge triples from ConceptNet and perform knowledge-aware reasoning while tracking the entities.
Edit intent: clarity
Original:  Our EBR consistently improves the performance of the Transformer-based NMT: +3 BLEU points on Sinhala-English and +2.0 BLEU points on IWSLT'17 French-English  tasks.
Revised:  Our EBR consistently improves the performance of the Transformer-based NMT: +3 BLEU points on Sinhala-English , +2.0 BLEU points on IWSLT'17 French-English tasks.
Edit intent: coherence
Original:  Our EBR consistently improves the performance of the Transformer-based NMT: +3 BLEU points on Sinhala-English and +2.0 BLEU points on IWSLT'17 French-English  tasks.
Revised:  Our EBR consistently improves the performance of the Transformer-based NMT: +3 BLEU points on Sinhala-English and +2.0 BLEU points on IWSLT'17 French-English , and +1.7 BLEU points on WMT'19 German-English tasks.
Edit intent: meaning-changed
Original:  Our EBR consistently improves the performance of the Transformer-based NMT: + 3 BLEU points on Sinhala-English , + 2.0 BLEU points on IWSLT'17 French-English, and + 1.7 BLEU points on WMT' 19 German-English tasks.
Revised:  We use both marginal energy models (over target sentence) and joint energy models (over both source and target sentences). Our EBR with the joint energy model consistently improves the performance of the Transformer-based NMT: + 3 BLEU points on Sinhala-English , + 2.0 BLEU points on IWSLT'17 French-English, and + 1.7 BLEU points on WMT' 19 German-English tasks.
Edit intent: meaning-changed
Original:  Our EBR consistently improves the performance of the Transformer-based NMT: + 3 BLEU points on Sinhala-English , + 2.0 BLEU points on IWSLT'17 French-English, and + 1.7 BLEU points on WMT' 19 German-English tasks.
Revised:  Our EBR consistently improves the performance of the Transformer-based NMT: + 4 BLEU points on Sinhala-English , + 2.0 BLEU points on IWSLT'17 French-English, and + 1.7 BLEU points on WMT' 19 German-English tasks.
Edit intent: meaning-changed
Original:  Our EBR consistently improves the performance of the Transformer-based NMT: + 3 BLEU points on Sinhala-English , + 2.0 BLEU points on IWSLT'17 French-English, and + 1.7 BLEU points on WMT' 19 German-English tasks.
Revised:  Our EBR consistently improves the performance of the Transformer-based NMT: + 3 BLEU points on IWSLT'14 German-English , + 2.0 BLEU points on IWSLT'17 French-English, and + 1.7 BLEU points on WMT' 19 German-English tasks.
Edit intent: meaning-changed
Original:  Our EBR consistently improves the performance of the Transformer-based NMT: + 3 BLEU points on Sinhala-English , + 2.0 BLEU points on IWSLT'17 French-English, and + 1.7 BLEU points on WMT' 19 German-English tasks.
Revised:  Our EBR consistently improves the performance of the Transformer-based NMT: + 3 BLEU points on Sinhala-English , + 3.0 BELU points on Sinhala-English, + 1.7 BLEU points on WMT' 19 German-English tasks.
Edit intent: meaning-changed
Original:  Our EBR consistently improves the performance of the Transformer-based NMT: + 3 BLEU points on Sinhala-English , + 2.0 BLEU points on IWSLT'17 French-English, and + 1.7 BLEU points on WMT' 19 German-English tasks.
Revised:  Our EBR consistently improves the performance of the Transformer-based NMT: + 3 BLEU points on Sinhala-English , + 2.0 BLEU points on IWSLT'17 French-English, and + 1.2 BLEU on WMT' 19 German-English tasks.
Edit intent: meaning-changed
Original:  Our EBR consistently improves the performance of the Transformer-based NMT: + 3 BLEU points on Sinhala-English , + 2.0 BLEU points on IWSLT'17 French-English, and + 1.7 BLEU points on WMT' 19 German-English tasks.
Revised:  Our EBR consistently improves the performance of the Transformer-based NMT: + 3 BLEU points on Sinhala-English , + 2.0 BLEU points on IWSLT'17 French-English, and + 1.7 BLEU points on WMT' 16 English-German tasks.
Edit intent: meaning-changed
Original:  Different from existing datasets which only contain post-response pairs, we include anonymized user IDs as well as timestamps. This enables the development of personalized dialogue models which depend on the availability of users' historical conversations. Furthermore, the scale of Pchatbot is significantly larger than existing  datasets, which might benefit the data-driven models.
Revised:  To adapt the raw dataset to dialogue systems, we elaborately normalize the raw dataset via processes such as anonymization, deduplication, segmentation, and filtering. The scale of Pchatbot is significantly larger than existing datasets, which might benefit the data-driven models.
Edit intent: clarity
Original:  Our preliminary experimental study shows that a personalized chatbot model trained on Pchatbot outperforms the corresponding ad-hoc chatbot models . We also demonstrate that using larger dataset improves the quality of dialog models .
Revised:  Besides, current dialogue datasets for personalized chatbot usually contain several persona sentences or attributes. Different from existing datasets, Pchatbot provides anonymized user IDs and timestamps for both posts and responses. This enables the development of personalized dialogue models that directly learn implicit user personality from the user's dialogue history. Our preliminary experimental study benchmarks several state-of-the-art dialogue models to provide a comparison for future work. The dataset can be publicly accessed at Github .
Edit intent: meaning-changed
Original:  This paper examines the problem of identifying fake content generated by lightweight deep learning models . A dataset containing human and machine-generated headlines was created and a user study indicated that humans were only able to identify the fake headlines in 45.3 \% of the cases.
Revised:  In the not so distant future, identifying fake content generated by lightweight deep learning models . A dataset containing human and machine-generated headlines was created and a user study indicated that humans were only able to identify the fake headlines in 45.3 \% of the cases.
Edit intent: clarity
Original:  This paper examines the problem of identifying fake content generated by lightweight deep learning models . A dataset containing human and machine-generated headlines was created and a user study indicated that humans were only able to identify the fake headlines in 45.3 \% of the cases.
Revised:  This paper examines the problem of identifying fake content generated by deep learning models . A dataset containing human and machine-generated headlines was created and a user study indicated that humans were only able to identify the fake headlines in 45.3 \% of the cases.
Edit intent: clarity
Original:  This paper examines the problem of identifying fake content generated by lightweight deep learning models . A dataset containing human and machine-generated headlines was created and a user study indicated that humans were only able to identify the fake headlines in 45.3 \% of the cases.
Revised:  This paper examines the problem of identifying fake content generated by lightweight deep learning models will play a key role in protecting users from misinformation. To this end, a dataset containing human and machine-generated headlines was created and a user study indicated that humans were only able to identify the fake headlines in 45.3 \% of the cases.
Edit intent: meaning-changed
Original:  This paper examines the problem of identifying fake content generated by lightweight deep learning models . A dataset containing human and machine-generated headlines was created and a user study indicated that humans were only able to identify the fake headlines in 45.3 \% of the cases.
Revised:  This paper examines the problem of identifying fake content generated by lightweight deep learning models . A dataset containing human and computer-generated headlines was created and a user study indicated that humans were only able to identify the fake headlines in 45.3 \% of the cases.
Edit intent: clarity
Original:  This paper examines the problem of identifying fake content generated by lightweight deep learning models . A dataset containing human and machine-generated headlines was created and a user study indicated that humans were only able to identify the fake headlines in 45.3 \% of the cases.
Revised:  This paper examines the problem of identifying fake content generated by lightweight deep learning models . A dataset containing human and machine-generated headlines was created and a user study indicated that humans were only able to identify the fake headlines in 47.8 \% of the cases.
Edit intent: meaning-changed
Original:  We find that the average correlation between sovereign bonds within the  COVID-19 period decreases, from the peak observed in the 2019-2020 period, where this trend is also reflected in all network filtering methods .
Revised:  We find that the average correlation between sovereign bonds within the mean correlation to decrease across all filtering methods during the COVID-19 period decreases, from the peak observed in the 2019-2020 period, where this trend is also reflected in all network filtering methods .
Edit intent: meaning-changed
Original:  We find that the average correlation between sovereign bonds within the  COVID-19 period decreases, from the peak observed in the 2019-2020 period, where this trend is also reflected in all network filtering methods . We also find variations between the movements of different network filtering methodsunder various network measures .
Revised:  We find that the average correlation between sovereign bonds within the COVID-19 period . We also observe a distinctive trend between filtering methods under multiple network centrality measures. We then relate the significance of economic and health variables towards filtered networks within the COVID-19 period. Under an exponential random graph model, we are able to identify key relations between economic groups across different filtering methods .
Edit intent: meaning-changed
Original: In this work, we develop a method for learning interpretable and thermodynamically stable partial differential equations (PDEs) based on the Conservation-dissipation Formalism of irreversible thermodynamics.
Revised: In this work, we develop a method for learning interpretable , thermodynamically stable and Galilean invariant partial differential equations (PDEs) based on the Conservation-dissipation Formalism of irreversible thermodynamics.
Edit intent: meaning-changed
Original: In a recent  work, Gartland and Lokshtanov [FOCS 2020] gave a quasi-polynomial-time algorithm for Maximum Weight Independent Set in P_t-free graphs, that is, graphs excluding a path on t vertices as an induced subgraph.
Revised: In a recent breakthrough work, Gartland and Lokshtanov [FOCS 2020] gave a quasi-polynomial-time algorithm for Maximum Weight Independent Set in P_t-free graphs, that is, graphs excluding a path on t vertices as an induced subgraph.
Edit intent: clarity
Original: In a recent  work, Gartland and Lokshtanov [FOCS 2020] gave a quasi-polynomial-time algorithm for Maximum Weight Independent Set in P_t-free graphs, that is, graphs excluding a path on t vertices as an induced subgraph.
Revised: In a recent work, Gartland and Lokshtanov [FOCS 2020] showed a quasi-polynomial-time algorithm for Maximum Weight Independent Set in P_t-free graphs, that is, graphs excluding a path on t vertices as an induced subgraph.
Edit intent: clarity
Original: In a recent  work, Gartland and Lokshtanov [FOCS 2020] gave a quasi-polynomial-time algorithm for Maximum Weight Independent Set in P_t-free graphs, that is, graphs excluding a path on t vertices as an induced subgraph.
Revised: In a recent work, Gartland and Lokshtanov [FOCS 2020] gave a quasi-polynomial-time algorithm for Maximum Weight Independent Set in P_t-free graphs, that is, graphs excluding a fixed path as an induced subgraph.
Edit intent: clarity
Original:  Since a P_t-free graph contains O (n^{t-1}) induced paths in total, branching on such a vertex and recursing independently on the connected components leads to a quasi-polynomial running time bound.
Revised:  Since a P_t-free graph contains \mathcal{O (n^{t-1}) induced paths in total, branching on such a vertex and recursing independently on the connected components leads to a quasi-polynomial running time bound.
Edit intent: meaning-changed
Original:  In a subsequent and very recent work, Gartland and Lokshtanov arXiv:2007.11402%DIFDELCMD < ] %%% extended their ideas to C_{> t-free graphs: graphs that do not contain a cycle on more than t vertices as an induced subgraph.
Revised:  arXiv:2007.11402%DIFDELCMD < ] %%% extended their ideas to C_{> t-free graphs: graphs that do not contain a cycle on more than t vertices as an induced subgraph.
Edit intent: coherence
Original:  In a subsequent and very recent work, Gartland and Lokshtanov arXiv:2007.11402%DIFDELCMD < ] %%% extended their ideas to C_{> t-free graphs: graphs that do not contain a cycle on more than t vertices as an induced subgraph.
Revised:  In a subsequent and very recent work, Gartland and Lokshtanov %DIFDELCMD < ] %%% extended their ideas to C_{> t-free graphs: graphs that do not contain a cycle on more than t vertices as an induced subgraph.
Edit intent: clarity
Original:  In a subsequent and very recent work, Gartland and Lokshtanov arXiv:2007.11402%DIFDELCMD < ] %%% extended their ideas to C_{> t-free graphs: graphs that do not contain a cycle on more than t vertices as an induced subgraph.
Revised:  In a subsequent and very recent work, Gartland and Lokshtanov arXiv:2007.11402%DIFDELCMD < ] %%% -free graphs: graphs that do not contain a cycle on more than t vertices as an induced subgraph.
Edit intent: clarity
Original:  We also show how to use }  the same approach  to obtain quasi-polynomial-time algorithms for related problems, including Maximum Weight Induced Matching and 3-Coloring , in P_t-free and C_{>t .
Revised:  We also show how to use } We also show that the same approach to obtain quasi-polynomial-time algorithms for related problems, including Maximum Weight Induced Matching and 3-Coloring , in P_t-free and C_{>t .
Edit intent: meaning-changed
Original:  We also show how to use }  the same approach  to obtain quasi-polynomial-time algorithms for related problems, including Maximum Weight Induced Matching and 3-Coloring , in P_t-free and C_{>t .
Revised:  We also show how to use } the same approach can be used to obtain quasi-polynomial-time algorithms for related problems, including Maximum Weight Induced Matching and 3-Coloring , in P_t-free and C_{>t .
Edit intent: clarity
Original:  We also show how to use }  the same approach  to obtain quasi-polynomial-time algorithms for related problems, including Maximum Weight Induced Matching and 3-Coloring , in P_t-free and C_{>t .
Revised:  We also show how to use } the same approach to obtain quasi-polynomial-time algorithms for related problems, including Maximum Weight Induced Matching and 3-Coloring .
Edit intent: coherence
Original: Translation is a fundamental cellular process in gene expression. During translation, complex micro-molecules called ribosomes sequentially scan the genetic information encoded in the mRNA molecule and transform it into a chain of amino-acids that is further processed to yield a functional protein. The speed of translation depends on the initiation, elongation, and termination rates of ribosomes along the mRNA.
Revised: The rate of mRNA translation depends on the initiation, elongation, and termination rates of ribosomes along the mRNA.
Edit intent: clarity
Original:  These rates depend on many ``local'' factors like the abundance of free ribosomes and cognate tRNA molecules in the vicinity of the mRNA . Consequently, copies of the same mRNA moleculelocated at different parts of the cell may be translated in different rates.
Revised:  These rates depend on many "local" factors like the abundance of free ribosomes and cognate tRNA molecules in the vicinity of the mRNA . Consequently, copies of the same mRNA moleculelocated at different parts of the cell may be translated in different rates.
Edit intent: fluency
Original:  These rates depend on many ``local'' factors like the abundance of free ribosomes and cognate tRNA molecules in the vicinity of the mRNA . Consequently, copies of the same mRNA moleculelocated at different parts of the cell may be translated in different rates.
Revised:  These rates depend on many ``local'' factors like the abundance of free ribosomes and tRNA molecules in the vicinity of the mRNA . Consequently, copies of the same mRNA moleculelocated at different parts of the cell may be translated in different rates.
Edit intent: clarity
Original:  These rates depend on many ``local'' factors like the abundance of free ribosomes and cognate tRNA molecules in the vicinity of the mRNA . Consequently, copies of the same mRNA moleculelocated at different parts of the cell may be translated in different rates.
Revised:  These rates depend on many ``local'' factors like the abundance of free ribosomes and cognate tRNA molecules in the vicinity of the mRNA molecule. All these factors are stochastic and their experimental measurements are also noisy.
Edit intent: meaning-changed
Original:  We develop a  theoretical framework for addressing this question that is based on: (1) considering a computational model for the flow of ribosomes along the mRNA, called the ribosome flow model, but with rates that are random variables ;
Revised:  We develop a new theoretical framework for addressing this question that is based on: (1) considering a computational model for the flow of ribosomes along the mRNA, called the ribosome flow model, but with rates that are random variables ;
Edit intent: clarity
Original:  We develop a  theoretical framework for addressing this question that is based on: (1) considering a computational model for the flow of ribosomes along the mRNA, called the ribosome flow model, but with rates that are random variables ; and (2) analyzing the model steady-state behaviour using tools from random matrix theory .
Revised:  We develop a theoretical framework for addressing this question by modeling the rates as identically and independently distributed random variables and using tools from random matrix theory .
Edit intent: clarity
Original:  and (2) analyzing the model steady-state behaviour using tools from random matrix theory . Our results show that if all the rates are modeled as i. i.d. random variables bounded from below by a value B then as the length of the mRNA increases the total protein production converges, with probability one, to a value that depends only on B.
Revised:  and (2) analyzing the model steady-state behaviour using tools from random matrix theory to analyze the steady-state production rate. The analysis reveals a principle of universality: the average protein production rate depends only on B.
Edit intent: clarity
Original:  Our results show that if all the rates are modeled as i. i.d. random variables bounded from below by a value B then as the length of the mRNA increases the total protein production converges, with probability one, to a value that depends only on B. This reveals a principle of universality: total protein production is insensitive to many of the details underlying the distribution of the random variables .
Revised:  Our results show that if all the rates are modeled as i. i.d. random variables bounded from below by a value B then as the length of the mRNA increases the total protein production converges, with probability one, to a value that depends only on the of the set of possible values that the random variable may attain. This explains how total protein production is insensitive to many of the details underlying the distribution of the random variables .
Edit intent: clarity
Original:  Results demonstrate viability both on real data for avocado and mango trees and virtual data with independently controlled sensor noise and tree spacing .
Revised:  Our methods for tree location and segmentation improved on existing methods with an F1 score of 0.774 and a v-measure of 0.915 respectively, while trunk matter classification performed poorly in absolute terms with an average F1 score of 0.490 on real data, though consistently outperformed existing methods and displayed a significantly shorter runtime .
Edit intent: meaning-changed
Original:  Hence, we propose and examine the ` slowest-first' scheme, whereby protein sequences evolved structural asymmetry to accelerate co-translational folding (CTF): the slowest-folding elements (e.g. \beta-sheets) are positioned near the N terminal so they have more time to fold during translation.
Revised:  Hence, we propose the ' slowest-first' scheme, whereby protein sequences evolved structural asymmetry to accelerate co-translational folding (CTF): the slowest-folding elements (e.g. \beta-sheets) are positioned near the N terminal so they have more time to fold during translation.
Edit intent: coherence
Original:  Hence, we propose and examine the ` slowest-first' scheme, whereby protein sequences evolved structural asymmetry to accelerate co-translational folding (CTF): the slowest-folding elements (e.g. \beta-sheets) are positioned near the N terminal so they have more time to fold during translation.
Revised:  Hence, we propose and examine the ` slowest-first' scheme, whereby protein sequences evolved structural asymmetry to accelerate CTF: the slowest of the cooperatively-folding segments are positioned near the N terminal so they have more time to fold during translation.
Edit intent: clarity
Original:  Hence, we propose and examine the ` slowest-first' scheme, whereby protein sequences evolved structural asymmetry to accelerate co-translational folding (CTF): the slowest-folding elements (e.g. \beta-sheets) are positioned near the N terminal so they have more time to fold during translation.
Revised:  Hence, we propose and examine the ` slowest-first' scheme, whereby protein sequences evolved structural asymmetry to accelerate co-translational folding (CTF): the slowest-folding elements (e.g. \beta-sheets) are positioned near the N terminus so they have more time to fold during translation.
Edit intent: clarity
Original: Several recent efforts have been devoted to enhancing pre-trained language models (PLMs) by utilizing extra heterogeneous knowledge in knowledge graphs (KGs) , and achieved consistent improvements on various knowledge-driven NLP tasks.
Revised: Several recent efforts have been devoted to enhancing pre-trained language models (PLMs) by utilizing extra heterogeneous knowledge in knowledge graphs (KGs) and achieved consistent improvements on various knowledge-driven NLP tasks.
Edit intent: fluency