wanyu/IteraTeR_full_sent huggingface dataset:
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 in every language pair.
Edit intent: clarity
Original:  We find that a BERT model with fine-tuning is strikingly successful at the hard generalization tasks we pose using this dataset, and our active manipulations help us to understand why: despite the densely interconnected nature of the BERT architecture, the learned model embeds modular, general theories of lexical entailment relations.
Revised:  We find that a BERT model is strikingly successful at the hard generalization tasks we pose using this dataset, and our active manipulations help us to understand why: despite the densely interconnected nature of the BERT architecture, the learned model embeds modular, general theories of lexical entailment relations.
Edit intent: clarity
Original:  We find that a BERT model with fine-tuning is strikingly successful at the hard generalization tasks we pose using this dataset, and our active manipulations help us to understand why: despite the densely interconnected nature of the BERT architecture, the learned model embeds modular, general theories of lexical entailment relations.
Revised:  We find that a BERT model with fine-tuning is strikingly successful at the systematic generalization task we pose using this dataset, and our active manipulations help us to understand why: despite the densely interconnected nature of the BERT architecture, the learned model embeds modular, general theories of lexical entailment relations.
Edit intent: clarity
Original:  We find that a BERT model with fine-tuning is strikingly successful at the hard generalization tasks we pose using this dataset, and our active manipulations help us to understand why: despite the densely interconnected nature of the BERT architecture, the learned model embeds modular, general theories of lexical entailment relations.
Revised:  We find that a BERT model with fine-tuning is strikingly successful at the hard generalization tasks we pose using this dataset, and our active manipulations of model-internal vectors help us understand why: despite the densely interconnected nature of the BERT architecture, the learned model embeds modular, general theories of lexical entailment relations.
Edit intent: meaning-changed
Original: In this paper, we present WiC-TSV (Target Sense Verification for Words in Context ) , a new multi-domain evaluation benchmark for  Word Sense Disambiguation (WSD) and Entity Linking (EL).
Revised: We present WiC-TSV (Target Sense Verification for Words in Context ) , a new multi-domain evaluation benchmark for Word Sense Disambiguation (WSD) and Entity Linking (EL).
Edit intent: coherence
Original: In this paper, we present WiC-TSV (Target Sense Verification for Words in Context ) , a new multi-domain evaluation benchmark for  Word Sense Disambiguation (WSD) and Entity Linking (EL).
Revised: In this paper, we present WiC-TSV Target Sense Verification for Words in Context ) , a new multi-domain evaluation benchmark for Word Sense Disambiguation (WSD) and Entity Linking (EL).
Edit intent: coherence
Original: In this paper, we present WiC-TSV (Target Sense Verification for Words in Context ) , a new multi-domain evaluation benchmark for  Word Sense Disambiguation (WSD) and Entity Linking (EL).
Revised: In this paper, we present WiC-TSV ( ) , a new multi-domain evaluation benchmark for Word Sense Disambiguation (WSD) and Entity Linking (EL).
Edit intent: coherence
Original: In this paper, we present WiC-TSV (Target Sense Verification for Words in Context ) , a new multi-domain evaluation benchmark for  Word Sense Disambiguation (WSD) and Entity Linking (EL).
Revised: In this paper, we present WiC-TSV (Target Sense Verification for Words in Context , a new multi-domain evaluation benchmark for Word Sense Disambiguation (WSD) and Entity Linking (EL).
Edit intent: fluency
Original: In this paper, we present WiC-TSV (Target Sense Verification for Words in Context ) , a new multi-domain evaluation benchmark for  Word Sense Disambiguation (WSD) and Entity Linking (EL).
Revised: In this paper, we present WiC-TSV (Target Sense Verification for Words in Context ) , a new multi-domain evaluation benchmark for Target Sense Verification of Words in Context. The benchmark is a new framework for evaluating Word Sense Disambiguation (WSD) and Entity Linking (EL).
Edit intent: meaning-changed
Original: In this paper, we present WiC-TSV (Target Sense Verification for Words in Context ) , a new multi-domain evaluation benchmark for  Word Sense Disambiguation (WSD) and Entity Linking (EL).
Revised: In this paper, we present WiC-TSV (Target Sense Verification for Words in Context ) , a new multi-domain evaluation benchmark for Word Sense Disambiguation and Entity Linking (EL).
Edit intent: clarity
Original: In this paper, we present WiC-TSV (Target Sense Verification for Words in Context ) , a new multi-domain evaluation benchmark for  Word Sense Disambiguation (WSD) and Entity Linking (EL). Our benchmark is different from conventional WSD and EL benchmarks for it  being independent of a general sense inventory, making it highly flexible for the evaluation of a diverse set of models and systems in different domains.
Revised: In this paper, we present WiC-TSV (Target Sense Verification for Words in Context ) , a new multi-domain evaluation benchmark for Word Sense Disambiguation (WSD) and Entity Linking , which is different from conventional WSD and EL benchmarks for it being independent of a general sense inventory, making it highly flexible for the evaluation of a diverse set of models and systems in different domains.
Edit intent: coherence
Original:  Our benchmark is different from conventional WSD and EL benchmarks for it  being independent of a general sense inventory, making it highly flexible for the evaluation of a diverse set of models and systems in different domains.
Revised:  Our benchmark is different from conventional WSD and EL benchmarks for it covering various domains, while being independent of a general sense inventory, making it highly flexible for the evaluation of a diverse set of models and systems in different domains.
Edit intent: meaning-changed
Original:  WiC-TSV is split into three tasks (systems get hypernymy or definitional or both hypernymy and definitional information about the target sense). Test data is available in four domains: general (WordNet), computer science, cocktails and medical concepts. Results show that existing state-of-the-art language models such as BERT can achieve a high performancein both in-domain data and out-of-domain data, but they still have room for improvement .
Revised:  WiC-TSV provides three different evaluation settings, depending on the input signals provided to the model. We set baseline performance on the dataset using state-of-the-art language models such as BERT can achieve a high performancein both in-domain data and out-of-domain data, but they still have room for improvement .
Edit intent: clarity
Original:  Results show that existing state-of-the-art language models such as BERT can achieve a high performancein both in-domain data and out-of-domain data, but they still have room for improvement .
Revised:  Results show that existing state-of-the-art language models. Experimental results show that even though these models can perform decently on the task, there remains a gap between machine and human performance, especially in out-of-domain data, but they still have room for improvement .
Edit intent: meaning-changed
Original: For the automatic evaluation of Generative Question Answering (genQA ) systems, it is essential to assess the correctness of the generated answers . However, n-gram similarity metrics, which are widely used to compare generated texts and references, are prone to misjudge fact-based assessments .
Revised: For the automatic evaluation of Generative Question Answering (genQA ) systems, it is essential to assess the correctness of generated answers due to the free-form of the answer .
Edit intent: meaning-changed
Original:  To alleviate this problem, we propose a new metric for evaluating the correctness of genQA . Specifically, the new metric assigns different weights on each token via keyphrase prediction, thereby judging whether a predicted answer sentence captures the key meaning of the human judge's ground-truth .
Revised:  To alleviate this problem, we propose a new metric for evaluating the correctness of GenQA . Specifically, the new metric assigns different weights on each token via keyphrase prediction, thereby judging whether a predicted answer sentence captures the key meaning of the human judge's ground-truth .
Edit intent: fluency
Original:  To alleviate this problem, we propose a new metric for evaluating the correctness of genQA . Specifically, the new metric assigns different weights on each token via keyphrase prediction, thereby judging whether a predicted answer sentence captures the key meaning of the human judge's ground-truth .
Revised:  To alleviate this problem, we propose a new metric for evaluating the correctness of genQA . Specifically, our new metric assigns different weights on each token via keyphrase prediction, thereby judging whether a predicted answer sentence captures the key meaning of the human judge's ground-truth .
Edit intent: fluency
Original:  To alleviate this problem, we propose a new metric for evaluating the correctness of genQA . Specifically, the new metric assigns different weights on each token via keyphrase prediction, thereby judging whether a predicted answer sentence captures the key meaning of the human judge's ground-truth .
Revised:  To alleviate this problem, we propose a new metric for evaluating the correctness of genQA . Specifically, the new metric assigns different weights to each token via keyphrase prediction, thereby judging whether a predicted answer sentence captures the key meaning of the human judge's ground-truth .
Edit intent: fluency
Original:  To alleviate this problem, we propose a new metric for evaluating the correctness of genQA . Specifically, the new metric assigns different weights on each token via keyphrase prediction, thereby judging whether a predicted answer sentence captures the key meaning of the human judge's ground-truth .
Revised:  To alleviate this problem, we propose a new metric for evaluating the correctness of genQA . Specifically, the new metric assigns different weights on each token via keyphrase prediction, thereby judging whether a generated answer sentence captures the key meaning of the human judge's ground-truth .
Edit intent: clarity
Original:  To alleviate this problem, we propose a new metric for evaluating the correctness of genQA . Specifically, the new metric assigns different weights on each token via keyphrase prediction, thereby judging whether a predicted answer sentence captures the key meaning of the human judge's ground-truth .
Revised:  To alleviate this problem, we propose a new metric for evaluating the correctness of genQA . Specifically, the new metric assigns different weights on each token via keyphrase prediction, thereby judging whether a predicted answer sentence captures the key meaning of the reference answer .
Edit intent: clarity
Original: Vision, as a central component of human perception, plays a fundamental role in shaping natural language . To better understand how text models are connected to our visual perceptions , we propose a method for examining the similarities between neural representations extracted from words in text and objects in images .
Revised: While large-scale language models have enjoyed great success recently, much remains to be understood about what is encoded in their representations. In this work , we propose a method for examining the similarities between neural representations extracted from words in text and objects in images .
Edit intent: clarity
Original:  Our approach uses a lightweight probing model that learns to map language representations of concrete words to the visual domain . We find that representations from models trained on purely textual data, such as BERT, can be nontrivially mapped to those of a vision model. Such mappings generalize to object categories that were never seen by the probe during training, unlike mappings learned from permuted or random representations .
Revised:  Our approach uses a lightweight probing model that learns to map language representations of concrete words to the visual domain , finding representations of many language models to be useful in retrieving semantically aligned image patches. In control experiments where language and visual representations are intentionally mismatched, we observe much weaker results. Furthermore, we examine the impact of textual context in our experiments, finding, for instance, that nouns accompanied by adjectives lead to more accurate retrieval .
Edit intent: meaning-changed
Original:  We show evidence of a surprising common ground with the visual domain, finding representations of many language models to be useful in retrieving semantically aligned image patches . In control experiments where language and visual representations are intentionally mismatched, we observe much weaker results. Furthermore, we examine the impact of textual context in our experiments, finding, for instance, that nouns accompanied by adjectives lead to more accurate retrieval. Finally, we show that the examined models substantially under-perform humans in retrieval  .
Revised:  We show evidence of a surprising common ground with the visual domain, finding representations of many language models to be useful in retrieving semantically aligned image patches , and explore the role of context in this process. Much weaker results are found in control experiments, attesting the selectivity of the probe. All examined models greatly under-perform humans in retrieval .
Edit intent: clarity
Original:  In this work, we characterize how contextual representations of concrete nouns extracted by trained language models relate to the physical properties of the objects they refer to. Our approach uses a probing model that examines how effective these language representations are in discerning between different visual representations.
Revised:  In this work, we characterize how contextual representations of concrete nouns are aligned with corresponding visual representations? We design a probing model that examines how effective these language representations are in discerning between different visual representations.
Edit intent: clarity
Original:  After confirmingquite a few untruthful instances in the datasets, this study hypothesizes that removing untruth-ful instances from the supervision data mayremedy the problem of the untruthful behav-iors of the model.
Revised:  After confirming quite a few untruthful instances in the datasets, this study hypothesizes that removing untruth-ful instances from the supervision data mayremedy the problem of the untruthful behav-iors of the model.
Edit intent: fluency
Original:  After confirmingquite a few untruthful instances in the datasets, this study hypothesizes that removing untruth-ful instances from the supervision data mayremedy the problem of the untruthful behav-iors of the model.
Revised:  After confirmingquite a few untruthful instances in the datasets, this study hypothesizes that removing untruthful instances from the supervision data mayremedy the problem of the untruthful behav-iors of the model.
Edit intent: fluency
Original:  After confirmingquite a few untruthful instances in the datasets, this study hypothesizes that removing untruth-ful instances from the supervision data mayremedy the problem of the untruthful behav-iors of the model.
Revised:  After confirmingquite a few untruthful instances in the datasets, this study hypothesizes that removing untruth-ful instances from the supervision data may remedy the problem of the untruthful behav-iors of the model.
Edit intent: fluency
Original:  After confirmingquite a few untruthful instances in the datasets, this study hypothesizes that removing untruth-ful instances from the supervision data mayremedy the problem of the untruthful behav-iors of the model.
Revised:  After confirmingquite a few untruthful instances in the datasets, this study hypothesizes that removing untruth-ful instances from the supervision data mayremedy the problem of the untruthful behaviors of the model.
Edit intent: fluency
Original:  Exper-imental results demonstrate that the headlinegeneration model trained on filtered supervi-sion data shows no clear difference in ROUGEscores but remarkable improvements in auto-matic and manual evaluations of the generatedheadlines .
Revised:  Experimental results demonstrate that the headlinegeneration model trained on filtered supervi-sion data shows no clear difference in ROUGEscores but remarkable improvements in auto-matic and manual evaluations of the generatedheadlines .
Edit intent: fluency
Original:  Exper-imental results demonstrate that the headlinegeneration model trained on filtered supervi-sion data shows no clear difference in ROUGEscores but remarkable improvements in auto-matic and manual evaluations of the generatedheadlines .
Revised:  Exper-imental results demonstrate that the headline generation model trained on filtered supervi-sion data shows no clear difference in ROUGEscores but remarkable improvements in auto-matic and manual evaluations of the generatedheadlines .
Edit intent: fluency
Original:  Exper-imental results demonstrate that the headlinegeneration model trained on filtered supervi-sion data shows no clear difference in ROUGEscores but remarkable improvements in auto-matic and manual evaluations of the generatedheadlines .
Revised:  Exper-imental results demonstrate that the headlinegeneration model trained on filtered supervision data shows no clear difference in ROUGEscores but remarkable improvements in auto-matic and manual evaluations of the generatedheadlines .
Edit intent: fluency
Original:  Exper-imental results demonstrate that the headlinegeneration model trained on filtered supervi-sion data shows no clear difference in ROUGEscores but remarkable improvements in auto-matic and manual evaluations of the generatedheadlines .
Revised:  Exper-imental results demonstrate that the headlinegeneration model trained on filtered supervi-sion data shows no clear difference in ROUGE scores but remarkable improvements in auto-matic and manual evaluations of the generatedheadlines .
Edit intent: fluency
Original:  Exper-imental results demonstrate that the headlinegeneration model trained on filtered supervi-sion data shows no clear difference in ROUGEscores but remarkable improvements in auto-matic and manual evaluations of the generatedheadlines .
Revised:  Exper-imental results demonstrate that the headlinegeneration model trained on filtered supervi-sion data shows no clear difference in ROUGEscores but remarkable improvements in automatic and manual evaluations of the generatedheadlines .
Edit intent: fluency
Original:  Exper-imental results demonstrate that the headlinegeneration model trained on filtered supervi-sion data shows no clear difference in ROUGEscores but remarkable improvements in auto-matic and manual evaluations of the generatedheadlines .
Revised:  Exper-imental results demonstrate that the headlinegeneration model trained on filtered supervi-sion data shows no clear difference in ROUGEscores but remarkable improvements in auto-matic and manual evaluations of the generated headlines .
Edit intent: fluency
Original:  The process of estimating MLA turns out to be beneficial for detecting multimodality, evaluating the soundness of risk allocations , and constructing more flexible risk allocations based on multiple risky scenarios . Properties of the conditional distribution and MLA are demonstrated in numerical experiments.
Revised:  Under multimodality, a single vector of allocations can be less sound. To overcome this issue, we investigate the so-called multimodalty adjustment to increase the soundness of risk allocations , and constructing more flexible risk allocations based on multiple risky scenarios . Properties of the conditional distribution and MLA are demonstrated in numerical experiments.
Edit intent: meaning-changed
Original:  The process of estimating MLA turns out to be beneficial for detecting multimodality, evaluating the soundness of risk allocations , and constructing more flexible risk allocations based on multiple risky scenarios . Properties of the conditional distribution and MLA are demonstrated in numerical experiments.
Revised:  The process of estimating MLA turns out to be beneficial for detecting multimodality, evaluating the soundness of risk allocations . Properties of the conditional distribution and MLA are demonstrated in numerical experiments.
Edit intent: clarity
Original:  The process of estimating MLA turns out to be beneficial for detecting multimodality, evaluating the soundness of risk allocations , and constructing more flexible risk allocations based on multiple risky scenarios . Properties of the conditional distribution and MLA are demonstrated in numerical experiments.
Revised:  The process of estimating MLA turns out to be beneficial for detecting multimodality, evaluating the soundness of risk allocations , and constructing more flexible risk allocations based on multiple risky scenarios . Properties of the conditional distribution , MLA and multimodality adjustment are demonstrated in numerical experiments.
Edit intent: clarity
Original: In this paper , we propose a new procedure to build a structural-factor model for a vector unit-root time series . For a p-dimensional unit-root process , we assume that each component consists of a set of common factors, which may be unit-root non-stationary, and a set of stationary components, which contain the cointegrations among the unit-root processes.
Revised: In this paper , we propose a new procedure to build a structural-factor model for a vector unit-root time series by postulating that a p-dimensional unit-root process , we assume that each component consists of a set of common factors, which may be unit-root non-stationary, and a set of stationary components, which contain the cointegrations among the unit-root processes.
Edit intent: meaning-changed
Original:  For a p-dimensional unit-root process , we assume that each component consists of a set of common factors, which may be unit-root non-stationary, and a set of stationary components, which contain the cointegrations among the unit-root processes.
Revised:  For a p-dimensional unit-root process is a nonsingular linear transformation of a set of common factors, which may be unit-root non-stationary, and a set of stationary components, which contain the cointegrations among the unit-root processes.
Edit intent: meaning-changed
Original:  For a p-dimensional unit-root process , we assume that each component consists of a set of common factors, which may be unit-root non-stationary, and a set of stationary components, which contain the cointegrations among the unit-root processes.
Revised:  For a p-dimensional unit-root process , we assume that each component consists of a set of unit-root non-stationary, and a set of stationary components, which contain the cointegrations among the unit-root processes.
Edit intent: clarity
Original:  For a p-dimensional unit-root process , we assume that each component consists of a set of common factors, which may be unit-root non-stationary, and a set of stationary components, which contain the cointegrations among the unit-root processes.
Revised:  For a p-dimensional unit-root process , we assume that each component consists of a set of common factors, which may be unit-root processes, a set of stationary components, which contain the cointegrations among the unit-root processes.
Edit intent: coherence