wanyu/IteraTeR_full_sent huggingface dataset:
Original:  This paper improves Neu-IR in such few-shot search scenarios by meta-adaptively training neural rankers with synthetic weak supervision. We first leverage contrastive query generation (ContrastQG) to synthesize more informative queries as in-domain weak relevance labels, and then filter them with meta adaptive learning to rank (MetaLTR) to better generalize neural rankers to the target few-shot domain . Experiments on three different search domains: web, news, and biomedical , demonstrate significantly improved few-shot accuracy of neural rankers with our weak supervision framework .
Revised:  This paper improves Neu-IR in such few-shot target domains. Drawing on source-domain massive relevance supervision, MetaAdaptRank contrastively synthesizes a large number of weak supervision signals for target domains and meta-learns to reweight these synthetic "weak" data based on their benefits to the target-domain ranking accuracy of Neu-IR models . Experiments on three different search domains: web, news, and biomedical , demonstrate significantly improved few-shot accuracy of neural rankers with our weak supervision framework .
Edit intent: clarity
Original:  We first leverage contrastive query generation (ContrastQG) to synthesize more informative queries as in-domain weak relevance labels, and then filter them with meta adaptive learning to rank (MetaLTR) to better generalize neural rankers to the target few-shot domain . Experiments on three different search domains: web, news, and biomedical , demonstrate significantly improved few-shot accuracy of neural rankers with our weak supervision framework .
Revised:  We first leverage contrastive query generation (ContrastQG) to synthesize more informative queries as in-domain weak relevance labels, and then filter them with meta adaptive learning to rank (MetaLTR) to better generalize neural rankers to the target few-shot domain . Experiments on three TREC benchmarks in the web, news, and biomedical , demonstrate significantly improved few-shot accuracy of neural rankers with our weak supervision framework .
Edit intent: clarity
Original:  We first leverage contrastive query generation (ContrastQG) to synthesize more informative queries as in-domain weak relevance labels, and then filter them with meta adaptive learning to rank (MetaLTR) to better generalize neural rankers to the target few-shot domain . Experiments on three different search domains: web, news, and biomedical , demonstrate significantly improved few-shot accuracy of neural rankers with our weak supervision framework .
Revised:  We first leverage contrastive query generation (ContrastQG) to synthesize more informative queries as in-domain weak relevance labels, and then filter them with meta adaptive learning to rank (MetaLTR) to better generalize neural rankers to the target few-shot domain . Experiments on three different search domains: web, news, and biomedical domains show that MetaAdaptRank significantly improves the few-shot accuracy of neural rankers with our weak supervision framework .
Edit intent: meaning-changed
Original:  We first leverage contrastive query generation (ContrastQG) to synthesize more informative queries as in-domain weak relevance labels, and then filter them with meta adaptive learning to rank (MetaLTR) to better generalize neural rankers to the target few-shot domain . Experiments on three different search domains: web, news, and biomedical , demonstrate significantly improved few-shot accuracy of neural rankers with our weak supervision framework .
Revised:  We first leverage contrastive query generation (ContrastQG) to synthesize more informative queries as in-domain weak relevance labels, and then filter them with meta adaptive learning to rank (MetaLTR) to better generalize neural rankers to the target few-shot domain . Experiments on three different search domains: web, news, and biomedical , demonstrate significantly improved few-shot ranking accuracy of Neu-IR models. Further analyses indicate that MetaAdaptRank thrives from both its contrastive weak data synthesis and meta-reweighted data selection .
Edit intent: meaning-changed
Original:  Using  regression analysis, I show that the vote share of the FPOE at the last national parliamentary elections is a strong predictor for the number of COVID-19 deaths  after the FPOE switched their policy stance, while there is no or even a negative correlation before the policy switch.
Revised:  Using panel regression analysis, I show that the vote share of the FPOE at the last national parliamentary elections is a strong predictor for the number of COVID-19 deaths after the FPOE switched their policy stance, while there is no or even a negative correlation before the policy switch.
Edit intent: meaning-changed
Original:  Using  regression analysis, I show that the vote share of the FPOE at the last national parliamentary elections is a strong predictor for the number of COVID-19 deaths  after the FPOE switched their policy stance, while there is no or even a negative correlation before the policy switch.
Revised:  Using regression analysis, I show that the vote share of the FPOE at the last national parliamentary elections is a strong predictor for the number of COVID-19 deaths per capita after the FPOE switched their policy stance, while there is no or even a negative correlation before the policy switch.
Edit intent: meaning-changed
Original:  Using  regression analysis, I show that the vote share of the FPOE at the last national parliamentary elections is a strong predictor for the number of COVID-19 deaths  after the FPOE switched their policy stance, while there is no or even a negative correlation before the policy switch.
Revised:  Using regression analysis, I show that the vote share of the FPOE at the last national parliamentary elections is a strong predictor for the number of COVID-19 deaths after the FPOE switched their policy stance, while there is no correlation before the policy switch.
Edit intent: clarity
Original:  Such a model can explain the nontrivial empirics: if mixing is sufficiently homophilic, an increase in the share of corona sceptics can cause an increase in the number of deaths without increasing the number of reported infections.
Revised:  Such a model can explain the puzzling empirics: if mixing is sufficiently homophilic, an increase in the share of corona sceptics can cause an increase in the number of deaths without increasing the number of reported infections.
Edit intent: clarity
Original:  Besides, to better understand relations, we employ a relation discrimination task which distinguishes whether two entity pairs are close or not in relational semantics . Experimental results demonstrate that our proposed ERICA framework achieves consistent improvements on several document-level language understanding tasks, including relation extraction and reading comprehension , especially under low resource setting.
Revised:  the relation discrimination task which distinguishes whether two entity pairs are close or not in relational semantics . Experimental results demonstrate that our proposed ERICA framework achieves consistent improvements on several document-level language understanding tasks, including relation extraction and reading comprehension , especially under low resource setting.
Edit intent: coherence
Original:  Besides, to better understand relations, we employ a relation discrimination task which distinguishes whether two entity pairs are close or not in relational semantics . Experimental results demonstrate that our proposed ERICA framework achieves consistent improvements on several document-level language understanding tasks, including relation extraction and reading comprehension , especially under low resource setting.
Revised:  Besides, to better understand relations, we employ a relation discrimination task to distinguish whether two relations are close or not in relational semantics . Experimental results demonstrate that our proposed ERICA framework achieves consistent improvements on several document-level language understanding tasks, including relation extraction and reading comprehension , especially under low resource setting.
Edit intent: clarity
Original:  Besides, to better understand relations, we employ a relation discrimination task which distinguishes whether two entity pairs are close or not in relational semantics . Experimental results demonstrate that our proposed ERICA framework achieves consistent improvements on several document-level language understanding tasks, including relation extraction and reading comprehension , especially under low resource setting.
Revised:  Besides, to better understand relations, we employ a relation discrimination task which distinguishes whether two entity pairs are close or not semantically, which involves complex relational reasoning . Experimental results demonstrate that our proposed ERICA framework achieves consistent improvements on several document-level language understanding tasks, including relation extraction and reading comprehension , especially under low resource setting.
Edit intent: clarity
Original:  Besides, to better understand relations, we employ a relation discrimination task which distinguishes whether two entity pairs are close or not in relational semantics . Experimental results demonstrate that our proposed ERICA framework achieves consistent improvements on several document-level language understanding tasks, including relation extraction and reading comprehension , especially under low resource setting.
Revised:  Besides, to better understand relations, we employ a relation discrimination task which distinguishes whether two entity pairs are close or not in relational semantics . Experimental results demonstrate that ERICA can improve typical PLMs (BERT and RoBERTa) on several language understanding tasks, including relation extraction and reading comprehension , especially under low resource setting.
Edit intent: meaning-changed
Original:  Besides, to better understand relations, we employ a relation discrimination task which distinguishes whether two entity pairs are close or not in relational semantics . Experimental results demonstrate that our proposed ERICA framework achieves consistent improvements on several document-level language understanding tasks, including relation extraction and reading comprehension , especially under low resource setting.
Revised:  Besides, to better understand relations, we employ a relation discrimination task which distinguishes whether two entity pairs are close or not in relational semantics . Experimental results demonstrate that our proposed ERICA framework achieves consistent improvements on several document-level language understanding tasks, including relation extraction , entity typing and question answering , especially under low resource setting.
Edit intent: clarity
Original:  Besides, to better understand relations, we employ a relation discrimination task which distinguishes whether two entity pairs are close or not in relational semantics . Experimental results demonstrate that our proposed ERICA framework achieves consistent improvements on several document-level language understanding tasks, including relation extraction and reading comprehension , especially under low resource setting. Meanwhile, ERICA achieves comparable or better performance on sentence-level tasks. We will release the datasets, source codes and pre-trained language models for further research explorations .
Revised:  Besides, to better understand relations, we employ a relation discrimination task which distinguishes whether two entity pairs are close or not in relational semantics . Experimental results demonstrate that our proposed ERICA framework achieves consistent improvements on several document-level language understanding tasks, including relation extraction and reading comprehension , especially under low-resource settings .
Edit intent: coherence
Original:  In Case Study 1, we analyze 1.3 million move decisions made by human experts and find that a positive form of adaptation to AI (learning) occurred after the experts could observe AI's reasoning processes rather than mere actions of AI.
Revised:  In Case Study 1, we analyze 1.3 million move decisions made by professional Go players and find that a positive form of adaptation to AI (learning) occurred after the experts could observe AI's reasoning processes rather than mere actions of AI.
Edit intent: clarity
Original:  In Case Study 1, we analyze 1.3 million move decisions made by human experts and find that a positive form of adaptation to AI (learning) occurred after the experts could observe AI's reasoning processes rather than mere actions of AI.
Revised:  In Case Study 1, we analyze 1.3 million move decisions made by human experts and find that a positive form of adaptation to AI (learning) occurred after the players could observe AI's reasoning processes rather than mere actions of AI.
Edit intent: clarity
Original:  In Case Study 1, we analyze 1.3 million move decisions made by professional Go players and find that a positive form of adaptation to AI (learning) occurred after the players could observe AI's reasoning processes rather than mere actions of AI.
Revised:  In Study 1, we analyze 1.3 million move decisions made by professional Go players and find that a positive form of adaptation to AI (learning) occurred after the players could observe AI's reasoning processes rather than mere actions of AI.
Edit intent: coherence
Original:  In Case Study 1, we analyze 1.3 million move decisions made by professional Go players and find that a positive form of adaptation to AI (learning) occurred after the players could observe AI's reasoning processes rather than mere actions of AI.
Revised:  In Case Study 1, we analyze 1.3 million move decisions made by professional Go players and find that a positive form of adaptation to AI (learning) occurred after the players could observe the reasoning processes of AI, rather than mere actions of AI.
Edit intent: clarity
Original:  In Case Study 2, we test whether our measure is sensitive enough to capture a negative form of adaptation to AI (cheating aided by AI), which occurred in a match between professional Go players.
Revised:  These findings based on our measure highlight the importance of explainability for human learning from AI. In Study 2, we test whether our measure is sensitive enough to capture a negative form of adaptation to AI (cheating aided by AI), which occurred in a match between professional Go players.
Edit intent: meaning-changed
Original: The effective lower bound on a short term interest rate may not constrain a central bank's capacity to achieve its objectives if unconventional monetary policy (UMP) is powerful enough. We formalize this `irrelevance hypothesis ' using a dynamic stochastic general equilibrium model with UMP and test it empirically for  the United States and Japan using a structural vector autoregressive model that includes variables subject to occasionally binding constraints.
Revised: The effective lower bound (ELB) on the short-term interest rate irrelevant. We develop an empirical test of this `irrelevance hypothesis ' using a dynamic stochastic general equilibrium model with UMP and test it empirically for the United States and Japan using a structural vector autoregressive model that includes variables subject to occasionally binding constraints.
Edit intent: clarity
Original:  We formalize this `irrelevance hypothesis ' using a dynamic stochastic general equilibrium model with UMP and test it empirically for  the United States and Japan using a structural vector autoregressive model that includes variables subject to occasionally binding constraints.
Revised:  We formalize this `irrelevance hypothesis ,' based on a simple idea that under the hypothesis, the short rate can be excluded in any empirical model that accounts for alternative measures of monetary policy. We develop a theoretical model that underpins this hypothesis, and test it empirically for the United States and Japan using a structural vector autoregressive model that includes variables subject to occasionally binding constraints.
Edit intent: meaning-changed
Original:  We formalize this `irrelevance hypothesis ' using a dynamic stochastic general equilibrium model with UMP and test it empirically for  the United States and Japan using a structural vector autoregressive model that includes variables subject to occasionally binding constraints.
Revised:  We formalize this `irrelevance hypothesis ' using a dynamic stochastic general equilibrium model with UMP and test it empirically for Japan and the United States and Japan using a structural vector autoregressive model that includes variables subject to occasionally binding constraints.
Edit intent: meaning-changed
Original:  We formalize this `irrelevance hypothesis ' using a dynamic stochastic general equilibrium model with UMP and test it empirically for  the United States and Japan using a structural vector autoregressive model that includes variables subject to occasionally binding constraints.
Revised:  We formalize this `irrelevance hypothesis ' using a dynamic stochastic general equilibrium model with UMP and test it empirically for the United States using a structural vector autoregressive model that includes variables subject to occasionally binding constraints.
Edit intent: clarity
Original:  We formalize this `irrelevance hypothesis ' using a dynamic stochastic general equilibrium model with UMP and test it empirically for  the United States and Japan using a structural vector autoregressive model that includes variables subject to occasionally binding constraints. The hypothesis is strongly rejected for both countries. However, a comparison of the impulse responses to a monetary policy shock across regimes shows that UMP has had strong delayed effects in each country .
Revised:  We formalize this `irrelevance hypothesis ' using a dynamic stochastic general equilibrium model with UMP and test it empirically for the United States and Japan using a structural vector autoregressive model with the ELB. For each country, we firmly reject the hypothesis but find that UMP has had strong delayed effects in each country .
Edit intent: clarity
Original: Due to the common belief that training deep transformers from scratch requires large datasets , people usually only use shallow and simple additional layers on top of pre-trained models during fine-tuning on small datasets. We provide evidence that this does not always need to be the case: with proper initialization and training techniques , the benefits of very deep transformers are shown to carry over to hard structural prediction tasks , even using small datasets .
Revised: Due to the common belief that training deep transformers from scratch requires large datasets , people usually only use shallow and simple additional layers on top of pre-trained models during fine-tuning . This work shows that this does not always need to be the case: with proper initialization and training techniques , the benefits of very deep transformers are shown to carry over to hard structural prediction tasks , even using small datasets .
Edit intent: clarity
Original:  We provide evidence that this does not always need to be the case: with proper initialization and training techniques , the benefits of very deep transformers are shown to carry over to hard structural prediction tasks , even using small datasets .
Revised:  We provide evidence that this does not always need to be the case: with proper initialization and training techniques , the benefits of very deep transformers are shown to carry over to challenging tasks with small datasets, including Text-to-SQL semantic parsing and logical reading comprehension .
Edit intent: meaning-changed
Original:  In particular, we successfully train 48 layers of transformers for a semantic parsing task. These comprise 24 fine-tuned transformer layers from pre-trained RoBERTa and 24 relation-aware transformer layers trained from scratch.
Revised:  In particular, we successfully train 48 layers of transformers , comprising 24 fine-tuned transformer layers from pre-trained RoBERTa and 24 relation-aware transformer layers trained from scratch.
Edit intent: clarity
Original:  To address these issues , we present the design and fabrication of Wi-Chlorian, which is a 'wireless' sensors that can be attached to an object or robot, like a sticker. Wi-Chlorian's sensor transduces force magnitude and location into phase changes of an incident RF signal , which is reflected back to enable measurement of force  and contact location .
Revised:  To address these issues , we present the design and fabrication of Wi-Chlorian, which is a 'wireless' sensor, sentient to contact force magnitude and location. WiForce achieves this by transducing force magnitude and location into phase changes of an incident RF signal , which is reflected back to enable measurement of force and contact location .
Edit intent: clarity
Original:  Wi-Chlorian's sensor transduces force magnitude and location into phase changes of an incident RF signal , which is reflected back to enable measurement of force  and contact location .
Revised:  Wi-Chlorian's sensor transduces force magnitude and location into phase changes of an incident RF signal of a backscattering tag. The phase changes are thus modulated into the backscattered RF signal, which enables measurement of force and contact location .
Edit intent: meaning-changed
Original:  Wi-Chlorian's sensor transduces force magnitude and location into phase changes of an incident RF signal , which is reflected back to enable measurement of force  and contact location . Wi-Chlorian 's sensor is designed to support wide-band frequencies all the way up to 3GHz . We evaluate the force sensing wirelessly in different environments, including in-body like , and achieve force ac-curacy of 0.3 N and contact location accuracy of 0.6 mm.
Revised:  Wi-Chlorian's sensor transduces force magnitude and location into phase changes of an incident RF signal , which is reflected back to enable measurement of force and contact location by inferring the phases of the reflected RF signal. WiForce 's sensor is designed to support wide-band frequencies all the way up to 3GHz . We evaluate the force sensing wirelessly in different environments, including in-body like , and achieve force ac-curacy of 0.3 N and contact location accuracy of 0.6 mm.
Edit intent: meaning-changed
Original:  Wi-Chlorian 's sensor is designed to support wide-band frequencies all the way up to 3GHz . We evaluate the force sensing wirelessly in different environments, including in-body like , and achieve force ac-curacy of 0.3 N and contact location accuracy of 0.6 mm.
Revised:  Wi-Chlorian 's sensor is designed to support wide-band frequencies all the way up to 3 GHz . We evaluate the force sensing wirelessly in different environments, including in-body like , and achieve force ac-curacy of 0.3 N and contact location accuracy of 0.6 mm.
Edit intent: fluency
Original:  Wi-Chlorian 's sensor is designed to support wide-band frequencies all the way up to 3GHz . We evaluate the force sensing wirelessly in different environments, including in-body like , and achieve force ac-curacy of 0.3 N and contact location accuracy of 0.6 mm.
Revised:  Wi-Chlorian 's sensor is designed to support wide-band frequencies all the way up to 3GHz . We evaluate the force sensing wirelessly in different environments, including through phantom tissue , and achieve force ac-curacy of 0.3 N and contact location accuracy of 0.6 mm.
Edit intent: meaning-changed
Original:  Wi-Chlorian 's sensor is designed to support wide-band frequencies all the way up to 3GHz . We evaluate the force sensing wirelessly in different environments, including in-body like , and achieve force ac-curacy of 0.3 N and contact location accuracy of 0.6 mm.
Revised:  Wi-Chlorian 's sensor is designed to support wide-band frequencies all the way up to 3GHz . We evaluate the force sensing wirelessly in different environments, including in-body like , and achieve force accuracy of 0.3 N and contact location accuracy of 0.6 mm.
Edit intent: fluency