wanyu/IteraTeR_full_sent huggingface dataset:
Original:  The largest share of COVID-related decreases in emissions are due to decreases in industry (157.9 Mt CO2, -7.1\%compared to 2019), followed by road transportation (145.7 Mt CO2, -8.3\% ), power generation (131.6 Mt CO2, -3.8 \%), residential (47.8 Mt CO2, -3.6 \%), fishing and maritime transport (35.5 Mt CO2, -13.3\%)and aviation (33.4 Mt CO2, -8.0 \%).
Revised:  The largest share of COVID-related decreases in emissions are due to decreases in industry (157.9 Mt CO2, -15.5\%), power (292.5 Mt CO2, -8.3\% ), power generation (131.6 Mt CO2, -3.8 \%), residential (47.8 Mt CO2, -3.6 \%), fishing and maritime transport (35.5 Mt CO2, -13.3\%)and aviation (33.4 Mt CO2, -8.0 \%).
Edit intent: meaning-changed
Original:  The largest share of COVID-related decreases in emissions are due to decreases in industry (157.9 Mt CO2, -7.1\%compared to 2019), followed by road transportation (145.7 Mt CO2, -8.3\% ), power generation (131.6 Mt CO2, -3.8 \%), residential (47.8 Mt CO2, -3.6 \%), fishing and maritime transport (35.5 Mt CO2, -13.3\%)and aviation (33.4 Mt CO2, -8.0 \%).
Revised:  The largest share of COVID-related decreases in emissions are due to decreases in industry (157.9 Mt CO2, -7.1\%compared to 2019), followed by road transportation (145.7 Mt CO2, -6.4\% compared to 2019), industry (136.2 Mt CO2, -3.8 \%), residential (47.8 Mt CO2, -3.6 \%), fishing and maritime transport (35.5 Mt CO2, -13.3\%)and aviation (33.4 Mt CO2, -8.0 \%).
Edit intent: meaning-changed
Original:  The largest share of COVID-related decreases in emissions are due to decreases in industry (157.9 Mt CO2, -7.1\%compared to 2019), followed by road transportation (145.7 Mt CO2, -8.3\% ), power generation (131.6 Mt CO2, -3.8 \%), residential (47.8 Mt CO2, -3.6 \%), fishing and maritime transport (35.5 Mt CO2, -13.3\%)and aviation (33.4 Mt CO2, -8.0 \%).
Revised:  The largest share of COVID-related decreases in emissions are due to decreases in industry (157.9 Mt CO2, -7.1\%compared to 2019), followed by road transportation (145.7 Mt CO2, -8.3\% ), power generation (131.6 Mt CO2, -4.4 \%), residential (47.8 Mt CO2, -3.6 \%), fishing and maritime transport (35.5 Mt CO2, -13.3\%)and aviation (33.4 Mt CO2, -8.0 \%).
Edit intent: meaning-changed
Original:  The largest share of COVID-related decreases in emissions are due to decreases in industry (157.9 Mt CO2, -7.1\%compared to 2019), followed by road transportation (145.7 Mt CO2, -8.3\% ), power generation (131.6 Mt CO2, -3.8 \%), residential (47.8 Mt CO2, -3.6 \%), fishing and maritime transport (35.5 Mt CO2, -13.3\%)and aviation (33.4 Mt CO2, -8.0 \%).
Revised:  The largest share of COVID-related decreases in emissions are due to decreases in industry (157.9 Mt CO2, -7.1\%compared to 2019), followed by road transportation (145.7 Mt CO2, -8.3\% ), power generation (131.6 Mt CO2, -3.8 \%), aviation (92.8 Mt CO2, -3.6 \%), fishing and maritime transport (35.5 Mt CO2, -13.3\%)and aviation (33.4 Mt CO2, -8.0 \%).
Edit intent: meaning-changed
Original:  The largest share of COVID-related decreases in emissions are due to decreases in industry (157.9 Mt CO2, -7.1\%compared to 2019), followed by road transportation (145.7 Mt CO2, -8.3\% ), power generation (131.6 Mt CO2, -3.8 \%), residential (47.8 Mt CO2, -3.6 \%), fishing and maritime transport (35.5 Mt CO2, -13.3\%)and aviation (33.4 Mt CO2, -8.0 \%).
Revised:  The largest share of COVID-related decreases in emissions are due to decreases in industry (157.9 Mt CO2, -7.1\%compared to 2019), followed by road transportation (145.7 Mt CO2, -8.3\% ), power generation (131.6 Mt CO2, -3.8 \%), residential (47.8 Mt CO2, -28.9 \%), fishing and maritime transport (35.5 Mt CO2, -13.3\%)and aviation (33.4 Mt CO2, -8.0 \%).
Edit intent: meaning-changed
Original:  The largest share of COVID-related decreases in emissions are due to decreases in industry (157.9 Mt CO2, -7.1\%compared to 2019), followed by road transportation (145.7 Mt CO2, -8.3\% ), power generation (131.6 Mt CO2, -3.8 \%), residential (47.8 Mt CO2, -3.6 \%), fishing and maritime transport (35.5 Mt CO2, -13.3\%)and aviation (33.4 Mt CO2, -8.0 \%).
Revised:  The largest share of COVID-related decreases in emissions are due to decreases in industry (157.9 Mt CO2, -7.1\%compared to 2019), followed by road transportation (145.7 Mt CO2, -8.3\% ), power generation (131.6 Mt CO2, -3.8 \%), residential (47.8 Mt CO2, -3.6 \%), residential (43.4 Mt CO2, -13.3\%)and aviation (33.4 Mt CO2, -8.0 \%).
Edit intent: meaning-changed
Original:  The largest share of COVID-related decreases in emissions are due to decreases in industry (157.9 Mt CO2, -7.1\%compared to 2019), followed by road transportation (145.7 Mt CO2, -8.3\% ), power generation (131.6 Mt CO2, -3.8 \%), residential (47.8 Mt CO2, -3.6 \%), fishing and maritime transport (35.5 Mt CO2, -13.3\%)and aviation (33.4 Mt CO2, -8.0 \%).
Revised:  The largest share of COVID-related decreases in emissions are due to decreases in industry (157.9 Mt CO2, -7.1\%compared to 2019), followed by road transportation (145.7 Mt CO2, -8.3\% ), power generation (131.6 Mt CO2, -3.8 \%), residential (47.8 Mt CO2, -3.6 \%), fishing and maritime transport (35.5 Mt CO2, -2.7\%), and international shipping (35.9 Mt CO2, -8.0 \%).
Edit intent: meaning-changed
Original:  The largest share of COVID-related decreases in emissions are due to decreases in industry (157.9 Mt CO2, -7.1\%compared to 2019), followed by road transportation (145.7 Mt CO2, -8.3\% ), power generation (131.6 Mt CO2, -3.8 \%), residential (47.8 Mt CO2, -3.6 \%), fishing and maritime transport (35.5 Mt CO2, -13.3\%)and aviation (33.4 Mt CO2, -8.0 \%).
Revised:  The largest share of COVID-related decreases in emissions are due to decreases in industry (157.9 Mt CO2, -7.1\%compared to 2019), followed by road transportation (145.7 Mt CO2, -8.3\% ), power generation (131.6 Mt CO2, -3.8 \%), residential (47.8 Mt CO2, -3.6 \%), fishing and maritime transport (35.5 Mt CO2, -13.3\%)and aviation (33.4 Mt CO2, -15 \%).
Edit intent: meaning-changed
Original:  Regionally, decreases in emissions from China were the largest and earliest ( -10.3 \%), followed by Europe (EU-27 & UK) ( -4.3 \%) and the U.S. ( -4.2 \%).
Revised:  Regionally, decreases in China were the largest and earliest ( -10.3 \%), followed by Europe (EU-27 & UK) ( -4.3 \%) and the U.S. ( -4.2 \%).
Edit intent: clarity
Original:  Regionally, decreases in emissions from China were the largest and earliest ( -10.3 \%), followed by Europe (EU-27 & UK) ( -4.3 \%) and the U.S. ( -4.2 \%).
Revised:  Regionally, decreases in emissions from China were the largest and earliest ( 234.5 Mt CO2,-6.9 \%), followed by Europe (EU-27 & UK) ( -4.3 \%) and the U.S. ( -4.2 \%).
Edit intent: meaning-changed
Original:  Regionally, decreases in emissions from China were the largest and earliest ( -10.3 \%), followed by Europe (EU-27 & UK) ( -4.3 \%) and the U.S. ( -4.2 \%).
Revised:  Regionally, decreases in emissions from China were the largest and earliest ( -10.3 \%), followed by Europe (EU-27 & UK) ( 138.3 Mt CO2, -12.0 \%) and the U.S. ( -4.2 \%).
Edit intent: meaning-changed
Original:  Regionally, decreases in emissions from China were the largest and earliest ( -10.3 \%), followed by Europe (EU-27 & UK) ( -4.3 \%) and the U.S. ( -4.2 \%).
Revised:  Regionally, decreases in emissions from China were the largest and earliest ( -10.3 \%), followed by Europe (EU-27 & UK) ( -4.3 \%) and the U.S. ( 162.4 Mt CO2, -9.5 \%).
Edit intent: meaning-changed
Original:  Relative decreases of regional CO2 emissions are consistent with regional nitrogen oxides concentrations observed by satellites and ground-based networks . Despite the unprecedented decreases in CO2 emissions and comparable decreases in economic activities, we monitored decreases in the carbon intensity (Emission per unit of GDP) in China (3.5\%), the U. S. (4.5\%) and Europe (5.4\%) over the first quarter, suggesting that carbon-intensive activities have been disproportionally impacted .
Revised:  Relative decreases of regional CO2 emissions are consistent with regional nitrogen oxides concentrations observed by satellites and ground-based networks , but the calculated signal of emissions decreases (about 1Gt CO2) will have little impacts (less than 0.13ppm by April 30, 2020) on the overserved global CO2 concertation. However, with observed fast CO2 recovery in China and partial re-opening globally, our findings suggest the longer-term effects on CO2 emissions are unknown and should be carefully monitored using multiple measures .
Edit intent: meaning-changed
Original:  This paper proposes a method named Filtering before Iteratively REferring (FIRE) for presenting the background knowledge of dialogue agents in retrieval-based chatbots. We first propose a pre-filter, which is composed of a context filter and a knowledge filter .
Revised:  This paper proposes a method named Filtering before Iteratively REferring (FIRE) for this task. In this method, a context filter and a knowledge filter .
Edit intent: clarity
Original:  We first propose a pre-filter, which is composed of a context filter and a knowledge filter . This pre-filter grounds the conversation on the knowledge and comprehends the knowledge according to the conversation by collecting the matching information between them bidirectionally, and then recognizing the important information in them accordingly .
Revised:  We first propose a pre-filter, which is composed of a context filter and a knowledge filter are first built, which derive knowledge-aware context representations and context-aware knowledge representations respectively by global and bidirectional attention. Besides, the entries irrelevant to the conversation by collecting the matching information between them bidirectionally, and then recognizing the important information in them accordingly .
Edit intent: meaning-changed
Original:  After that, iteratively referring is performed between the context and the response , as well as between the knowledge and the response , in order to collect the deep and wide matching information . Experimental results show that the FIRE model outperforms previous methods by margins larger than 2.8\% on original personas and 4.1\% on revised personas on the PERSONA-CHAT dataset , as well as 3.1\% on the CMU_DoG dataset in terms of top-1 accuracy  .
Revised:  After that, iteratively referring is performed between context and response representations as well as between the knowledge and the response , in order to collect the deep and wide matching information . Experimental results show that the FIRE model outperforms previous methods by margins larger than 2.8\% on original personas and 4.1\% on revised personas on the PERSONA-CHAT dataset , as well as 3.1\% on the CMU_DoG dataset in terms of top-1 accuracy .
Edit intent: fluency
Original:  After that, iteratively referring is performed between the context and the response , as well as between the knowledge and the response , in order to collect the deep and wide matching information . Experimental results show that the FIRE model outperforms previous methods by margins larger than 2.8\% on original personas and 4.1\% on revised personas on the PERSONA-CHAT dataset , as well as 3.1\% on the CMU_DoG dataset in terms of top-1 accuracy  .
Revised:  After that, iteratively referring is performed between the context and the response , as well as between knowledge and response representations , in order to collect the deep and wide matching information . Experimental results show that the FIRE model outperforms previous methods by margins larger than 2.8\% on original personas and 4.1\% on revised personas on the PERSONA-CHAT dataset , as well as 3.1\% on the CMU_DoG dataset in terms of top-1 accuracy .
Edit intent: clarity
Original:  After that, iteratively referring is performed between the context and the response , as well as between the knowledge and the response , in order to collect the deep and wide matching information . Experimental results show that the FIRE model outperforms previous methods by margins larger than 2.8\% on original personas and 4.1\% on revised personas on the PERSONA-CHAT dataset , as well as 3.1\% on the CMU_DoG dataset in terms of top-1 accuracy  .
Revised:  After that, iteratively referring is performed between the context and the response , as well as between the knowledge and the response , in order to collect deep matching features for scoring response candidates . Experimental results show that the FIRE model outperforms previous methods by margins larger than 2.8\% on original personas and 4.1\% on revised personas on the PERSONA-CHAT dataset , as well as 3.1\% on the CMU_DoG dataset in terms of top-1 accuracy .
Edit intent: clarity
Original:  After that, iteratively referring is performed between the context and the response , as well as between the knowledge and the response , in order to collect the deep and wide matching information . Experimental results show that the FIRE model outperforms previous methods by margins larger than 2.8\% on original personas and 4.1\% on revised personas on the PERSONA-CHAT dataset , as well as 3.1\% on the CMU_DoG dataset in terms of top-1 accuracy  .
Revised:  After that, iteratively referring is performed between the context and the response , as well as between the knowledge and the response , in order to collect the deep and wide matching information . Experimental results show that FIRE outperforms previous methods by margins larger than 2.8\% on original personas and 4.1\% on revised personas on the PERSONA-CHAT dataset , as well as 3.1\% on the CMU_DoG dataset in terms of top-1 accuracy .
Edit intent: clarity
Original:  After that, iteratively referring is performed between the context and the response , as well as between the knowledge and the response , in order to collect the deep and wide matching information . Experimental results show that the FIRE model outperforms previous methods by margins larger than 2.8\% on original personas and 4.1\% on revised personas on the PERSONA-CHAT dataset , as well as 3.1\% on the CMU_DoG dataset in terms of top-1 accuracy  .
Revised:  After that, iteratively referring is performed between the context and the response , as well as between the knowledge and the response , in order to collect the deep and wide matching information . Experimental results show that the FIRE model outperforms previous methods by margins larger than 2.8\% and 4.1\% on revised personas on the PERSONA-CHAT dataset , as well as 3.1\% on the CMU_DoG dataset in terms of top-1 accuracy .
Edit intent: clarity
Original:  After that, iteratively referring is performed between the context and the response , as well as between the knowledge and the response , in order to collect the deep and wide matching information . Experimental results show that the FIRE model outperforms previous methods by margins larger than 2.8\% on original personas and 4.1\% on revised personas on the PERSONA-CHAT dataset , as well as 3.1\% on the CMU_DoG dataset in terms of top-1 accuracy  .
Revised:  After that, iteratively referring is performed between the context and the response , as well as between the knowledge and the response , in order to collect the deep and wide matching information . Experimental results show that the FIRE model outperforms previous methods by margins larger than 2.8\% on original personas and 4.1\% on the PERSONA-CHAT dataset , as well as 3.1\% on the CMU_DoG dataset in terms of top-1 accuracy .
Edit intent: clarity
Original:  After that, iteratively referring is performed between the context and the response , as well as between the knowledge and the response , in order to collect the deep and wide matching information . Experimental results show that the FIRE model outperforms previous methods by margins larger than 2.8\% on original personas and 4.1\% on revised personas on the PERSONA-CHAT dataset , as well as 3.1\% on the CMU_DoG dataset in terms of top-1 accuracy  .
Revised:  After that, iteratively referring is performed between the context and the response , as well as between the knowledge and the response , in order to collect the deep and wide matching information . Experimental results show that the FIRE model outperforms previous methods by margins larger than 2.8\% on original personas and 4.1\% on revised personas on the PERSONA-CHAT dataset with original and revised personas respectively, and margins larger than 3.1\% on the CMU_DoG dataset in terms of top-1 accuracy .
Edit intent: meaning-changed
Original:  After that, iteratively referring is performed between the context and the response , as well as between the knowledge and the response , in order to collect the deep and wide matching information . Experimental results show that the FIRE model outperforms previous methods by margins larger than 2.8\% on original personas and 4.1\% on revised personas on the PERSONA-CHAT dataset , as well as 3.1\% on the CMU_DoG dataset in terms of top-1 accuracy  .
Revised:  After that, iteratively referring is performed between the context and the response , as well as between the knowledge and the response , in order to collect the deep and wide matching information . Experimental results show that the FIRE model outperforms previous methods by margins larger than 2.8\% on original personas and 4.1\% on revised personas on the PERSONA-CHAT dataset , as well as 3.1\% on the CMU_DoG dataset in terms of top-1 accuracy . We also show that FIRE is more interpretable by visualizing the knowledge grounding process .
Edit intent: meaning-changed
Original: We propose the use of a sequence-to-sequence paraphraser for automatic machine translation evaluation . The paraphrasertakes a human reference as input and then force-decodes and scores an MT system output .
Revised: We propose the use of a sequence-to-sequence paraphraser for automatic machine translation evaluation as one of scoring machine translation output with a sequence-to-sequence paraphraser, conditioned on a human reference as input and then force-decodes and scores an MT system output .
Edit intent: meaning-changed
Original:  We propose training the aforementioned paraphraser as a multilingual NMT system, treating paraphrasing as a zero-shot "language pair" (e.g., Russian to Russian). We denote our paraphraser "unbiased" because the mode of our model 's output probability is centered around a copy of the input sequence, which in our case represent the best case scenario where the MT system output matches a human reference.
Revised:  We propose training the aforementioned paraphraser as a multilingual NMT system, treating paraphrasing as a zero-shot "language pair" (e.g., Czech to Czech). This results in the paraphraser 's output probability is centered around a copy of the input sequence, which in our case represent the best case scenario where the MT system output matches a human reference.
Edit intent: clarity
Original:  We denote our paraphraser "unbiased" because the mode of our model 's output probability is centered around a copy of the input sequence, which in our case represent the best case scenario where the MT system output matches a human reference.
Revised:  We denote our paraphraser "unbiased" because the mode of our model 's output mode being centered around a copy of the input sequence, which in our case represent the best case scenario where the MT system output matches a human reference.
Edit intent: clarity
Original:  We denote our paraphraser "unbiased" because the mode of our model 's output probability is centered around a copy of the input sequence, which in our case represent the best case scenario where the MT system output matches a human reference.
Revised:  We denote our paraphraser "unbiased" because the mode of our model 's output probability is centered around a copy of the input sequence, which represents the best case scenario where the MT system output matches a human reference.
Edit intent: clarity
Original:  Our method is simple and intuitive, and our single model (trained in 39 languages) outperforms or statistically ties with all prior metrics on the WMT19 segment-level shared metrics task in all languages , excluding Gujarati where the model had no training data  .
Revised:  Our method is simple and intuitive, and does not require human judgements for training. Our single model (trained in 39 languages) outperforms or statistically ties with all prior metrics on the WMT19 segment-level shared metrics task in all languages , excluding Gujarati where the model had no training data .
Edit intent: meaning-changed
Original:  Our method is simple and intuitive, and our single model (trained in 39 languages) outperforms or statistically ties with all prior metrics on the WMT19 segment-level shared metrics task in all languages , excluding Gujarati where the model had no training data  .
Revised:  Our method is simple and intuitive, and our single model (trained in 39 languages) outperforms or statistically ties with all prior metrics on the WMT 2019 segment-level shared metrics task in all languages , excluding Gujarati where the model had no training data .
Edit intent: fluency
Original:  Our method is simple and intuitive, and our single model (trained in 39 languages) outperforms or statistically ties with all prior metrics on the WMT19 segment-level shared metrics task in all languages , excluding Gujarati where the model had no training data  .
Revised:  Our method is simple and intuitive, and our single model (trained in 39 languages) outperforms or statistically ties with all prior metrics on the WMT19 segment-level shared metrics task in all languages ( excluding Gujarati where the model had no training data .
Edit intent: fluency
Original:  Our method is simple and intuitive, and our single model (trained in 39 languages) outperforms or statistically ties with all prior metrics on the WMT19 segment-level shared metrics task in all languages , excluding Gujarati where the model had no training data  .
Revised:  Our method is simple and intuitive, and our single model (trained in 39 languages) outperforms or statistically ties with all prior metrics on the WMT19 segment-level shared metrics task in all languages , excluding Gujarati where the model had no training data ) .
Edit intent: fluency
Original:  We also explore using our model conditioned on the source instead of the reference, and find that it outperforms every quality estimation as a metric system from the WMT19 shared task on quality estimation by a statistically significant margin in every language pair.
Revised:  We also explore using our model for the task of quality estimation as a metric--conditioning on the source instead of the reference, and find that it outperforms every quality estimation as a metric system from the WMT19 shared task on quality estimation by a statistically significant margin in every language pair.
Edit intent: clarity
Original:  We also explore using our model conditioned on the source instead of the reference, and find that it outperforms every quality estimation as a metric system from the WMT19 shared task on quality estimation by a statistically significant margin in every language pair.
Revised:  We also explore using our model conditioned on the source instead of the reference--and find that it outperforms every quality estimation as a metric system from the WMT19 shared task on quality estimation by a statistically significant margin in every language pair.
Edit intent: fluency
Original:  We also explore using our model conditioned on the source instead of the reference, and find that it outperforms every quality estimation as a metric system from the WMT19 shared task on quality estimation by a statistically significant margin in every language pair.
Revised:  We also explore using our model conditioned on the source instead of the reference, and find that it significantly outperforms every submission to the WMT 2019 shared task on quality estimation by a statistically significant margin in every language pair.
Edit intent: clarity