wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We provide a novel characterization: the parallel trends assumption holds under all strictly monotonic transformations of the outcome if and only if a stronger "parallel trends"-type condition holds on the entire distribution of untreated potential outcomes.
Revised:  We provide a novel characterization: the parallel trends assumption holds under all strictly monotonic transformations of the outcome if and only if a stronger "parallel trends"-type condition holds for the cumulative distribution function of untreated potential outcomes.
Edit intent: clarity
Original:  This condition is satisfied if (i) treatment is as-if randomly assigned, (ii) the distribution of potential outcomes is stationary, or (iii) treatment is as-if randomly assigned among a subset of the population and the remainder of the population has stationary potential outcomes .
Revised:  This condition is satisfied if and essentially only if the population can be partitioned into a subgroup for which treatment is effectively randomly assigned and a remaining subgroup for which the distribution of untreated potential outcomes is stable over time .
Edit intent: meaning-changed
Original: Pandemics such as COVID-19 have lethal potential for inflicting long-lasting cyclic devastations if required preventive, curative and reformative steps are not taken up in time which puts forth mammoth multi-dimensional challenges for survival before mankind.
Revised:  COVID-19 have lethal potential for inflicting long-lasting cyclic devastations if required preventive, curative and reformative steps are not taken up in time which puts forth mammoth multi-dimensional challenges for survival before mankind.
Edit intent: coherence
Original: Pandemics such as COVID-19 have lethal potential for inflicting long-lasting cyclic devastations if required preventive, curative and reformative steps are not taken up in time which puts forth mammoth multi-dimensional challenges for survival before mankind. Scientists and policymakers all around are striving to achieve R \leq 1 alongside having less number of CoVID-19 patients. Lockdowns all across the globe have been implemented for the sake of social physical distancing. However, even if the desired R value status is achieved it becomes nowhere near safe. As normal social activity and inter-regional travel resumes, danger of contraction of the virus from undetected asymptomatic carriers and reactivation of the virus in previously affected patients looms over. The virus poses further threat due to its chances of resurgence, its mutative and adaptive nature thereby giving limited medical respite. The problems intensify with increasing population density whilst varying with several socio-economic-geo-cultural and human activity parameters.
Revised: Pandemics such as COVID-19 have lethal potential for inflicting long-lasting cyclic devastations if required preventive, curative and reformative steps are not taken up in time which puts forth mammoth multi-dimensional fundamental challenges in securing general socio-economic wellbeing of the society. The problems intensify with increasing population density whilst varying with several socio-economic-geo-cultural and human activity parameters.
Edit intent: clarity
Original:  Such zoonotic pandemics unravel the primary challenges of all countries in securing the general wellbeing of the society. Ensuring a mechanism for policy designs envisaging crisis scenarios through continuous analysis of real-time region-specific data about societal activities and disease / health indicators can be the only solution.
Revised:  These problems directly highlight the urgent need for accomplishing the interdependent United Nations Sustainable Development Goals (SDGs) to ensure that in future we do not enter into vicious loops of contracting newer zoonotic viruses and need not search for their vaccines while incurring socio-economic havoc. Behavioural changes in human activities/responses are indispensable for achieving the interdependent SDGs. Using root cause analysis approach, we have developed a yearly assessment framework for viably analysing and identifying requisite region-specific data about societal activities and disease / health indicators can be the only solution.
Edit intent: meaning-changed
Original:  Ensuring a mechanism for policy designs envisaging crisis scenarios through continuous analysis of real-time region-specific data about societal activities and disease / health indicators can be the only solution. An approach perspective is discussed for addressing the tightly-coupled UN Sustainable goals (2, 3, 6, 12 and 13) for developing a general-scale computational agent-based model to estimate the downstream and upstream measures for achieving requisite societal behavioural changes with the prognostic knowledge concerning the conditions and options for future scenarios of stable sustainability .
Revised:  Ensuring a mechanism for policy designs envisaging crisis scenarios through continuous analysis of real-time region-specific data about societal activities and disease / upstream socio-economic policies to reach the SDGs. The framework makes use of an infographic bar chart representation based on the normalised values of 20 human activity/impact parameters classified under three categories as - negative, limiting and positive. With a holistic view encompassing the SDGs, we illustrate through this framework the impact and urgent need of region-specific human behavioural reforms. This framework enables the foresight about policies regarding their potential in bringing down the negative parameter values to the desired zero level for accomplishing the SDGs through planetary health .
Edit intent: clarity
Original:  Our results set the first baseline for the QReCC dataset with F1 of 19.07 , compared to the human upper bound of 74.47 , indicating the difficulty of the setup and a large room for improvement.
Revised:  Our results set the first baseline for the QReCC dataset with F1 of 19.10 , compared to the human upper bound of 74.47 , indicating the difficulty of the setup and a large room for improvement.
Edit intent: meaning-changed
Original:  Our results set the first baseline for the QReCC dataset with F1 of 19.07 , compared to the human upper bound of 74.47 , indicating the difficulty of the setup and a large room for improvement.
Revised:  Our results set the first baseline for the QReCC dataset with F1 of 19.07 , compared to the human upper bound of 75.45 , indicating the difficulty of the setup and a large room for improvement.
Edit intent: meaning-changed
Original:  We use the branching-process approach for contact tracing to analyze the impact of super-spreader events on the effect of contact tracing  . Roughly speaking, we find that contact tracing is more efficient in the presence of super-spreaders if the fraction of symptomatics is small, the tracing probability is high, or the latency period is distinctively larger than the incubation period.
Revised:  We use the branching-process approach for contact tracing to analyze the impact of super-spreader events on the effect of contact tracing . Here we neglect a tracing delay . Roughly speaking, we find that contact tracing is more efficient in the presence of super-spreaders if the fraction of symptomatics is small, the tracing probability is high, or the latency period is distinctively larger than the incubation period.
Edit intent: meaning-changed
Original: The main subject and the associated verb in English must agree in grammatical number as per the Subject-Verb Agreement (SVA) phenomenon. It has been found that the presence of a noun between the verb and the main subject , whose grammatical number is opposite to that of the main subject, can cause speakers to produce a verb that agrees with the intervening nounrather than the main noun;
Revised: Previous work suggests that RNNs trained on natural language corpora can capture number agreement well for simple sentences but perform less well when sentences contain agreement attractors: intervening nouns between the verb and the main subject , whose grammatical number is opposite to that of the main subject, can cause speakers to produce a verb that agrees with the intervening nounrather than the main noun;
Edit intent: meaning-changed
Original:  It has been found that the presence of a noun between the verb and the main subject , whose grammatical number is opposite to that of the main subject, can cause speakers to produce a verb that agrees with the intervening nounrather than the main noun; the former thus acts as an agreement attractor. Such attractors have also been shown to pose a challenge for RNN models without explicit hierarchical bias to perform well on SVA tasks.Previous work suggests that syntactic cues in the input can aid such models to choose hierarchical rules over linear rules for number agreement. In this work, we investigate the effects of the choice of training data, training algorithm, and architecture on hierarchical generalization. We observe that the models under consideration fail to perform well on sentences with no agreement attractor when trained solely on natural sentences with at least one attractor.
Revised:  It has been found that the presence of a noun between the verb and the main subject with grammatical number opposite to the models under consideration fail to perform well on sentences with no agreement attractor when trained solely on natural sentences with at least one attractor.
Edit intent: clarity
Original:  We observe that the models under consideration fail to perform well on sentences with no agreement attractor when trained solely on natural sentences with at least one attractor.
Revised:  We observe that the latter. This suggests these models may not learn the actual syntax of agreement, but rather infer shallower heuristics such as `agree with the recent noun'. In this work, we investigate RNN models with varying inductive biases trained on selectively chosen `hard' agreement instances, i.e., sentences with at least one agreement attractor. For these the verb number cannot be predicted using a simple linear heuristic, and hence they might help provide the model additional cues for hierarchical syntax. If RNNs can learn the underlying agreement rules when trained on such hard instances, then they should generalize well to other sentences, including simpler ones. However, we observe that several RNN types, including the ONLSTM which has a soft structural inductive bias, surprisingly fail to perform well on sentences with no agreement attractor when trained solely on natural sentences with at least one attractor.
Edit intent: meaning-changed
Original:  We observe that the models under consideration fail to perform well on sentences with no agreement attractor when trained solely on natural sentences with at least one attractor. Even in the presence of this biased training set, implicit hierarchical bias in the architecture (as in the Ordered Neurons LSTM) is not enough to capture syntax-sensitive dependencies. These results suggest that current RNNs do not capture the underlying hierarchical rules of natural language , but rather use shallower heuristics for their predictions .
Revised:  We observe that the models under consideration fail to perform well on sentences with no agreement attractor when trained solely on sentences with attractors. We analyze how these selectively trained RNNs compare to the baseline (training on a natural distribution of agreement attractors) along the dimensions of number agreement accuracy, representational similarity, and performance across different syntactic constructions. Our findings suggest that RNNs trained on our hard agreement instances still do not capture the underlying hierarchical rules of natural language , but rather use shallower heuristics for their predictions .
Edit intent: meaning-changed
Original:  These results suggest that current RNNs do not capture the underlying hierarchical rules of natural language , but rather use shallower heuristics for their predictions .
Revised:  These results suggest that current RNNs do not capture the underlying hierarchical rules of natural language , but rather tend to overfit the training distribution in a way which leads them to perform poorly on `easy' out-of-distribution instances. Thus, while RNNs are powerful models which can pick up non-trivial dependency patterns, inducing them to do so at the level of syntax rather than surface remains a challenge .
Edit intent: meaning-changed
Original:  To improve the estimation efficiency, we propose a new method that combines experimental and observational data by leveraging covariates that satisfy the first-stage relevance condition in the observational data .
Revised:  To improve the estimation efficiency, we propose a new method that combines experimental and observational datasets when 1) units from these two datasets are sampled from the same population and 2) some characteristics of these units are observed. I show that if these characteristics can partially explain treatment assignment in the observational data .
Edit intent: meaning-changed
Original:  To improve the estimation efficiency, we propose a new method that combines experimental and observational data by leveraging covariates that satisfy the first-stage relevance condition in the observational data . We first use the observational data to derive a biased estimate of the causal effect and then correct this bias using the experimental data. We show thatthis bias-corrected estimator is uncorrelated with the benchmark estimator that only uses the experimental data, and can thus be combined with the benchmark estimator to improve efficiency.
Revised:  To improve the estimation efficiency, we propose a new method that combines experimental and observational data by leveraging covariates that satisfy the first-stage relevance condition in the observational data , they can be used to derive moment restrictions that, in combination with the experimental data, and can thus be combined with the benchmark estimator to improve efficiency.
Edit intent: clarity
Original:  We show thatthis bias-corrected estimator is uncorrelated with the benchmark estimator that only uses the experimental data, and can thus be combined with the benchmark estimator to improve efficiency. Under a common experimental design that randomly assigns units into the experimental group, our method can reduce the variance by up to 50\% , so that only half of the experimental sample is required to attain the same accuracy.
Revised:  We show thatthis bias-corrected estimator is uncorrelated with the benchmark estimator that only uses the experimental data, improve estimation efficiency. I outline three estimators (weighting, shrinkage, or GMM) for implementing this strategy, and show that my methods can reduce variance by up to 50\% , so that only half of the experimental sample is required to attain the same accuracy.
Edit intent: meaning-changed
Original:  Under a common experimental design that randomly assigns units into the experimental group, our method can reduce the variance by up to 50\% , so that only half of the experimental sample is required to attain the same accuracy.
Revised:  Under a common experimental design that randomly assigns units into the experimental group, our method can reduce the variance by up to 50\% in typical experimental designs; therefore, only half of the experimental sample is required to attain the same accuracy.
Edit intent: meaning-changed
Original:  Under a common experimental design that randomly assigns units into the experimental group, our method can reduce the variance by up to 50\% , so that only half of the experimental sample is required to attain the same accuracy. This accuracy can be further improved if the experimental design accounts for this relevant first-stage covariate and select the experimental group differently .
Revised:  Under a common experimental design that randomly assigns units into the experimental group, our method can reduce the variance by up to 50\% , so that only half of the experimental sample is required to attain the same statistical precision. If researchers are allowed to design experiments differently, I show that they can further improve the precision by directly leveraging this correlation between characteristics and assignment. I apply the method to a search listing dataset from Expedia that studies the causal effect of search rankings, and show that my method can substantially improve the precision .
Edit intent: coherence
Original:  This problem naturally arises in contexts such as scheduling virus tests or quarantining individuals to curb a spreading epidemic; detecting fake news spreading on online networks by manually inspecting posted articles; and targeted marketing where the objective is to encourage the spread of a product. Curbing the spread and constraining the fraction of infected population becomes challenging when only a fraction of the population can be tested or quarantined. To address this challenge, we formulate this setup as a sequential decision problem over a graph .
Revised:  This problem naturally arises in contexts such as scheduling virus tests to curb an epidemic; targeted marketing in order to promote a product; and manually inspecting posts to detect fake news spreading on social networks. We formulate this setup as a sequential decision problem over a graph .
Edit intent: clarity
Original:  In face of an exponential state space, combinatorial action space and partial observability, we design RLGN, a novel tractable Reinforcement Learning (RL) scheme to prioritize which nodes should be tested , using Graph Neural Networks (GNNs) to rank the graph nodes.
Revised:  In face of an exponential state space, combinatorial action space and partial observability, we design a novel tractable Reinforcement Learning (RL) scheme to prioritize which nodes should be tested , using Graph Neural Networks (GNNs) to rank the graph nodes.
Edit intent: clarity
Original:  In face of an exponential state space, combinatorial action space and partial observability, we design RLGN, a novel tractable Reinforcement Learning (RL) scheme to prioritize which nodes should be tested , using Graph Neural Networks (GNNs) to rank the graph nodes.
Revised:  In face of an exponential state space, combinatorial action space and partial observability, we design RLGN, a novel tractable scheme to control dynamical processes on temporal graphs. We successfully apply our approach to two popular problems that fall into our framework: prioritizing which nodes should be tested , using Graph Neural Networks (GNNs) to rank the graph nodes.
Edit intent: meaning-changed
Original:  In face of an exponential state space, combinatorial action space and partial observability, we design RLGN, a novel tractable Reinforcement Learning (RL) scheme to prioritize which nodes should be tested , using Graph Neural Networks (GNNs) to rank the graph nodes. We evaluate this approach in three types of social-networks: community-structured, preferential attachment, and based on statistics from real cellular tracking.RLGN consistently outperforms all baselines in our experiments. It suggests that prioritizing tests using RL on temporal graphs can increase the number of healthy people by 25%DIFDELCMD < \\%%% %DIF < and contain the epidemic 30\\% more often than supervised approaches and 2.5\times more often than non-learned baselines using the same resources.abstract 
Revised:  In face of an exponential state space, combinatorial action space and partial observability, we design RLGN, a novel tractable Reinforcement Learning (RL) scheme to prioritize which nodes should be tested %DIFDELCMD < \\%%% %DIF < and contain the epidemic 30\\% more often than supervised approaches and 2.5\times more often than non-learned baselines using the same resources.abstract 
Edit intent: meaning-changed
Original:  It suggests that prioritizing tests using RL on temporal graphs can increase the number of healthy people by 25%DIFDELCMD < \\%%% %DIF < and contain the epidemic 30\\% more often than supervised approaches and 2.5\times more often than non-learned baselines using the same resources.abstract 
Revised:  It suggests that prioritizing tests using RL on temporal graphs can increase the number of healthy people by 25%DIFDELCMD < \\%%% %DIF < and contain the epidemic 30\\% more often than supervised approaches and 2.5\times more often than non-learned baselines using the same resources.abstract in order to curb the spread of an epidemic, and influence maximization on a graph.
Edit intent: meaning-changed
Original:  In this paper we propose schemes for incorporating explicit word-level gender inflection tags into NMT. We explore the potential of this gender-inflection controlled translation when the gender feature can be determined from a human reference,  assessing on English-to-Spanish and English-to-German translation.
Revised:  In this paper we propose schemes for incorporating explicit word-level gender inflection tags into NMT. We explore the potential of this gender-inflection controlled translation when the gender feature can be determined from a human reference, or when a test sentence can be automatically gender-tagged, assessing on English-to-Spanish and English-to-German translation.
Edit intent: meaning-changed
Original: I characterize the consumer-optimal market segmentation in competitive markets with differentiated products . I show that this segmentation is public---in that each firm observes the same market segments---and takes a simple form: in each market segment, there is a dominant firm favored by all consumers in that segment.
Revised: I characterize the consumer-optimal market segmentation in competitive markets where multiple firms selling differentiated products to consumers with unit demand. This segmentation is public---in that each firm observes the same market segments---and takes a simple form: in each market segment, there is a dominant firm favored by all consumers in that segment.
Edit intent: meaning-changed
Original: In a persuasion environment where both players are ambiguity averse, Beauch\^ene, Li and Li (2019) show that the sender can make strictly more profits from sending ambiguous signals if the receiver cares only about his interim payoff.
Revised: In a persuasion game, if both the sender and receiver are ambiguity averse, Beauch\^ene, Li and Li (2019) show that the sender can make strictly more profits from sending ambiguous signals if the receiver cares only about his interim payoff.
Edit intent: clarity
Original: In a persuasion environment where both players are ambiguity averse, Beauch\^ene, Li and Li (2019) show that the sender can make strictly more profits from sending ambiguous signals if the receiver cares only about his interim payoff.
Revised: In a persuasion environment where both players are ambiguity averse, can the sender benefit from sending ambiguous signals if the receiver cares only about his interim payoff.
Edit intent: clarity
Original: In a persuasion environment where both players are ambiguity averse, Beauch\^ene, Li and Li (2019) show that the sender can make strictly more profits from sending ambiguous signals if the receiver cares only about his interim payoff. As in the presence of ambiguity , the receiver may not be dynamically consistent.
Revised: In a persuasion environment where both players are ambiguity averse, Beauch\^ene, Li and Li (2019) show that the sender can make strictly more profits from sending ambiguous messages? This paper shows that the sender cannot benefit from introducing ambiguity if the receiver is dynamically consistent.
Edit intent: clarity
Original:  This paper studies ambiguous persuasion when the receiver' s goal is to maximize his ex-ante payoff. First of all , if the receiver is dynamically consistent, I show the sender cannot make any profits more than Bayesian persuasion.
Revised:  This result is obtained when both players' preferences are represented by the Maxmin Expected Utility (MEU) criterion. However , if the receiver is dynamically consistent, I show the sender cannot make any profits more than Bayesian persuasion.
Edit intent: meaning-changed