wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We propose to disentangle counterfactual generation from its use cases, i.e., gather general-purpose counterfactuals first, and then select them for specific applications. We frame the automated counterfactual generation as text generation, and finetune GPT-2 into a generator , Polyjuice, which produces fluent and diverse counterfactuals.
Revised:  We propose to disentangle counterfactual generation from its use cases, i.e., gather general-purpose counterfactual generator that allows for control over perturbation types and locations, trained by finetuning GPT-2 into a generator , Polyjuice, which produces fluent and diverse counterfactuals.
Edit intent: meaning-changed
Original:  We frame the automated counterfactual generation as text generation, and finetune GPT-2 into a generator , Polyjuice, which produces fluent and diverse counterfactuals. Our method also allows control over where perturbations happen and what they do .
Revised:  We frame the automated counterfactual generation as text generation, and finetune GPT-2 on multiple datasets of paired sentences .
Edit intent: meaning-changed
Original:  We show Polyjuice supports multiple use cases: by generating diverse counterfactuals for humans to label, Polyjuice helps produce high-quality datasets for model training and evaluation , requiring 40\% less human effort .
Revised:  We show that Polyjuice produces diverse sets of realistic counterfactuals, which in turn are useful in various distinct applications: improving training and evaluation , requiring 40\% less human effort .
Edit intent: clarity
Original:  We show Polyjuice supports multiple use cases: by generating diverse counterfactuals for humans to label, Polyjuice helps produce high-quality datasets for model training and evaluation , requiring 40\% less human effort . When used to generate explanations, Polyjuice helps augment feature attribution methods to reveal models' erroneous behaviors .
Revised:  We show Polyjuice supports multiple use cases: by generating diverse counterfactuals for humans to label, Polyjuice helps produce high-quality datasets for model training and evaluation on three different tasks (with around 70\% less annotation effort than manual generation), augmenting state-of-the-art explanation techniques, and supporting systematic counterfactual error analysis by revealing behaviors easily missed by human experts .
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Original: Current open-domain question answering (QA) systems often follow a Retriever-Reader (R2) architecture, where the retriever first retrieves relevant passages and the reader then reads the retrieved passages to form an answer.
Revised: Current open-domain question answering systems often follow a Retriever-Reader (R2) architecture, where the retriever first retrieves relevant passages and the reader then reads the retrieved passages to form an answer.
Edit intent: clarity
Original: Current open-domain question answering (QA) systems often follow a Retriever-Reader (R2) architecture, where the retriever first retrieves relevant passages and the reader then reads the retrieved passages to form an answer.
Revised: Current open-domain question answering (QA) systems often follow a Retriever-Reader architecture, where the retriever first retrieves relevant passages and the reader then reads the retrieved passages to form an answer.
Edit intent: clarity
Original:  In this paper, we propose a simple and effective passage reranking method,  Reader-guIDEd Reranker ( Rider ), which does not involve any training and reranks the retrieved passages solely based on the top predictions of the reader before reranking.
Revised:  In this paper, we propose a simple and effective passage reranking method, named Reader-guIDEd Reranker ( Rider ), which does not involve any training and reranks the retrieved passages solely based on the top predictions of the reader before reranking.
Edit intent: clarity
Original:  In this paper, we propose a simple and effective passage reranking method,  Reader-guIDEd Reranker ( Rider ), which does not involve any training and reranks the retrieved passages solely based on the top predictions of the reader before reranking.
Revised:  In this paper, we propose a simple and effective passage reranking method, Reader-guIDEd Reranker ( RIDER ), which does not involve any training and reranks the retrieved passages solely based on the top predictions of the reader before reranking.
Edit intent: fluency
Original:  In this paper, we propose a simple and effective passage reranking method,  Reader-guIDEd Reranker ( Rider ), which does not involve any training and reranks the retrieved passages solely based on the top predictions of the reader before reranking.
Revised:  In this paper, we propose a simple and effective passage reranking method, Reader-guIDEd Reranker ( Rider ), which does not involve training and reranks the retrieved passages solely based on the top predictions of the reader before reranking.
Edit intent: clarity
Original:  We show that Rider , despite its simplicity, achieves 10 to 20 absolute gains in top-1 retrieval accuracy and 1 to 4 Exact Match (EM) score gains without refining the retriever or reader.
Revised:  We show that RIDER , despite its simplicity, achieves 10 to 20 absolute gains in top-1 retrieval accuracy and 1 to 4 Exact Match (EM) score gains without refining the retriever or reader.
Edit intent: fluency
Original:  We show that Rider , despite its simplicity, achieves 10 to 20 absolute gains in top-1 retrieval accuracy and 1 to 4 Exact Match (EM) score gains without refining the retriever or reader.
Revised:  We show that Rider , despite its simplicity, achieves 10 to 20 absolute gains in top-1 retrieval accuracy and 1 to 4 Exact Match (EM) gains without refining the retriever or reader.
Edit intent: clarity
Original:  In particular, Rider achieves 48.3 EM on the Natural Questions dataset and 66.4  on the TriviaQA dataset when only 1,024 tokens (7.8 passages on average) are used as the reader input  .
Revised:  In addition, RIDER, without any training, outperforms state-of-the-art transformer-based supervised rerankers. Remarkably, RIDER achieves 48.3 EM on the Natural Questions dataset and 66.4 on the TriviaQA dataset when only 1,024 tokens (7.8 passages on average) are used as the reader input .
Edit intent: meaning-changed
Original:  In particular, Rider achieves 48.3 EM on the Natural Questions dataset and 66.4  on the TriviaQA dataset when only 1,024 tokens (7.8 passages on average) are used as the reader input  .
Revised:  In particular, Rider achieves 48.3 EM on the Natural Questions dataset and 66.4 EM on the TriviaQA dataset when only 1,024 tokens (7.8 passages on average) are used as the reader input .
Edit intent: fluency
Original:  In particular, Rider achieves 48.3 EM on the Natural Questions dataset and 66.4  on the TriviaQA dataset when only 1,024 tokens (7.8 passages on average) are used as the reader input  .
Revised:  In particular, Rider achieves 48.3 EM on the Natural Questions dataset and 66.4 on the TriviaQA dataset when only 1,024 tokens (7.8 passages on average) are used as the reader input after passage reranking .
Edit intent: clarity
Original:  The developed methods can also be used for HyperLogLog sketches and allow estimation of joint quantities such as the intersection size with a smaller error compared to the common estimation approach based on the inclusion-exclusion principle .
Revised:  The presented joint estimator can also be used for HyperLogLog sketches and allow estimation of joint quantities such as the intersection size with a smaller error compared to the common estimation approach based on the inclusion-exclusion principle .
Edit intent: meaning-changed
Original:  The developed methods can also be used for HyperLogLog sketches and allow estimation of joint quantities such as the intersection size with a smaller error compared to the common estimation approach based on the inclusion-exclusion principle .
Revised:  The developed methods can also be applied to other data structures such as MinHash, HyperLogLog, or HyperMinHash, where it even performs better than the corresponding state-of-the-art estimators in many cases .
Edit intent: meaning-changed
Original:  To illustrate our theoretic results, we investigate the negotiation between two firms , deriving inter-personal equilibria explicitly . In particular, it shows that coercive power in negotiation depends crucially on the impatience levels of the two firms.
Revised:  To illustrate our theoretic results, we investigate the negotiation between two firms , deriving inter-personal equilibria explicitly , we find that coercive power in negotiation depends crucially on the impatience levels of the two firms.
Edit intent: coherence
Original:  We study the ability of box embeddings, which represent entity types as d-dimensional hyperrectangles, to represent hierarchies of fine-grained entity type labels even when these relationships are not defined explicitly in the ontology.
Revised:  We study the ability of box embeddings, which embed concepts as d-dimensional hyperrectangles, to represent hierarchies of fine-grained entity type labels even when these relationships are not defined explicitly in the ontology.
Edit intent: clarity
Original:  We study the ability of box embeddings, which represent entity types as d-dimensional hyperrectangles, to represent hierarchies of fine-grained entity type labels even when these relationships are not defined explicitly in the ontology.
Revised:  We study the ability of box embeddings, which represent entity types as d-dimensional hyperrectangles, to capture hierarchies of types even when these relationships are not defined explicitly in the ontology.
Edit intent: clarity
Original:  In addition to competitive typing performance, our box-based model shows better performance in prediction consistency (predicting a supertype and a subtype together) and confidence (i.e., calibration), implying that the box-based model captures the latent type hierarchies better than the vector-based model does.
Revised:  In addition to competitive typing performance, our box-based model shows better performance in prediction consistency (predicting a supertype and a subtype together) and confidence (i.e., calibration), demonstrating that the box-based model captures the latent type hierarchies better than the vector-based model does.
Edit intent: clarity
Original:  Our study focuses on testing these models for the  elicitation of conspiracy theories and comparing these generations to human-written theories from Reddit. We also introduce a new dataset consisting of conspiracy theory topics , machine-generated conspiracy theories , and human-written conspiracy theories.
Revised:  Our study focuses on testing these models for the elicitation of conspiracy theories without access to the model's training data? We highlight the difficulties of this task and discuss it in the context of memorization, generalization, and hallucination. Utilizing a new dataset consisting of conspiracy theory topics , machine-generated conspiracy theories , and human-written conspiracy theories.
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Original:  We also introduce a new dataset consisting of conspiracy theory topics , machine-generated conspiracy theories , and human-written conspiracy theories. Our experiments show that many well-known conspiracy theory topics are deeply rooted in the pre-trained language models, and can become more prevalent through different model settings .
Revised:  We also introduce a new dataset consisting of conspiracy theory topics , machine-generated conspiracy theories helps us discover that many conspiracy theories are deeply rooted in the pre-trained language models, and can become more prevalent through different model settings .
Edit intent: clarity
Original:  Our experiments show that many well-known conspiracy theory topics are deeply rooted in the pre-trained language models, and can become more prevalent through different model settings .
Revised:  Our experiments show that many well-known conspiracy theory topics are deeply rooted in the pretrained language models. Our experiments demonstrate a relationship between model parameters such as size and temperature and their propensity to generate conspiracy theory text. These results indicate the need for a more thorough review of NLG applications before release and an in-depth discussion of the drawbacks of memorization in generative language models .
Edit intent: meaning-changed
Original: A stable matching between two sets of agents (such as students and colleges) has received a great deal of attention in the literature. For an empirical study of matching markets, the main statistics of interest are  the matching frequencies, which show the fraction of students of a given type who match with colleges of a given type.
Revised: In an empirical study of matching markets, the main statistics of interest are the matching frequencies, which show the fraction of students of a given type who match with colleges of a given type.
Edit intent: clarity
Original:  For an empirical study of matching markets, the main statistics of interest are  the matching frequencies, which show the fraction of students of a given type who match with colleges of a given type.
Revised:  For an empirical study of a two-sided large matching market (such as a college admissions problem), the primary statistics of interest are the matching frequencies, which show the fraction of students of a given type who match with colleges of a given type.
Edit intent: meaning-changed
Original:  For an empirical study of matching markets, the main statistics of interest are  the matching frequencies, which show the fraction of students of a given type who match with colleges of a given type.
Revised:  For an empirical study of matching markets, the main statistics of interest are often the empirical matching probabilities which represent the matching frequencies, which show the fraction of students of a given type who match with colleges of a given type.
Edit intent: meaning-changed
Original:  We introduce a notion of partial homogeneity of preferences to express correlated priorities of colleges over students, and demonstrate the relation between partial homogeneity and concentration of measure. Our concentration inequality immediately yields the rate of convergence for the law of large numbers for matching frequencies .
Revised:  We introduce a notion of partial homogeneity of preferences to express correlated priorities of colleges over students, and show that the more the preferences are aligned, the more the matching frequencies tend to concentrate on their expected value given the colleges' types. This means that the empirical matching probabilities become more robust to realizations of students' preferences when the colleges' preferences are more aligned .
Edit intent: clarity
Original:  We introduce a notion of partial homogeneity of preferences to express the degree of alignment in the preferences of colleges over students, and show that the more the preferences are aligned, the more the  matching frequencies tend to concentrate on their expected value given the colleges' types.
Revised:  We introduce a notion of partial homogeneity of preferences to express the degree of alignment in the preferences of colleges over students, and show that the more the preferences are aligned, the more the empirical matching frequencies tend to concentrate on their expected value given the colleges' types.
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Original:  This hypothesis is confirmed by a series of semantic probing tasks on which derivational segmentation consistently outperforms BERT 's WordPiece segmentationby a large margin .
Revised:  This hypothesis is confirmed by a series of semantic probing tasks on which DelBERT (Derivation leveraging BERT), a model with derivational input segmentation, substantially outperforms BERT with WordPiece segmentation .
Edit intent: meaning-changed
Original:  This is particularly the case in discussions of fairness and accountability in systems exposed to the general public. However, previous work has suggested that a trade-off exists between greater system transparency and user confusion, where ` too much information' clouds a reader's understanding of what a system description means.
Revised:  Unfortunately, it is a nebulous concept, difficult to both define and quantify. Previous work has suggested that a trade-off exists between greater system transparency and user confusion, where ` too much information' clouds a reader's understanding of what a system description means.
Edit intent: meaning-changed
Original:  In this work we address these two issues by proposing a framework for quantifying transparencyin system descriptions and apply it to analyze the trade-off between transparencyand end-user confusionusing NLP conference abstracts.
Revised:  In this setting, the degree to which the necessary information is conveyed represents the description's transparency, and the level of expertise needed by the third party corresponds to potential user confusion. We introduce two neural language model-based probabilistic metrics to model these factors, and demonstrate that they correlate with user and expert opinions of system transparency, making them a valid objective proxy. Finally, we apply these metrics to study the relationships between transparency, confusion, and user perceptions in a corpus of NLP demo abstracts.
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Original: Multi-hop reasoning (i.e., reasoning across two or more documents) at scale is a key step toward NLP models that can exhibit broad world knowledgeby leveraging large collections of documents. We propose Baleen, a system that improves the robustness and scalability of multi-hop reasoning over current approaches.
Revised: Multi-hop reasoning (i.e., reasoning across two or more documents) at scale is a key step toward NLP models that leverage large corpora to exhibit broad knowledge. To retrieve evidence passages, multi-hop models must contend with a fast-growing search space across the hops, represent complex queries that combine multiple information needs, and resolve ambiguity about the best order in which to hop between training passages. We tackle these problems via Baleen, a system that improves the robustness and scalability of multi-hop reasoning over current approaches.
Edit intent: meaning-changed
Original:  We propose Baleen, a system that improves the robustness and scalability of multi-hop reasoning over current approaches. Baleen introduces a per-hop condensed retrievalpipeline to mitigate the size of the search space, a focused late interaction retriever (FliBERT) that can model complex multi-hop queries, and a weak supervision strategy, latent hop ordering, to learn from limited signal about which documents to retrieve for a query .
Revised:  We propose Baleen, a system that improves the robustness and scalability of multi-hop retrieval. To tame the search space, we propose condensed retrieval, a pipeline that summarizes the retrieved passages after each hop into a single compact context. To model complex queries, we introduce a focused late interaction retriever (FliBERT) that can model complex multi-hop queries, and a weak supervision strategy, latent hop ordering, to learn from limited signal about which documents to retrieve for a query .
Edit intent: clarity
Original:  Baleen introduces a per-hop condensed retrievalpipeline to mitigate the size of the search space, a focused late interaction retriever (FliBERT) that can model complex multi-hop queries, and a weak supervision strategy, latent hop ordering, to learn from limited signal about which documents to retrieve for a query .
Revised:  Baleen introduces a per-hop condensed retrievalpipeline to mitigate the size of the search space, a focused late interaction retriever that allows different parts of the same query representation to match disparate relevant passages. Lastly, to infer the hopping dependencies among unordered training passages, we devise latent hop ordering, to learn from limited signal about which documents to retrieve for a query .
Edit intent: meaning-changed
Original:  Baleen introduces a per-hop condensed retrievalpipeline to mitigate the size of the search space, a focused late interaction retriever (FliBERT) that can model complex multi-hop queries, and a weak supervision strategy, latent hop ordering, to learn from limited signal about which documents to retrieve for a query .
Revised:  Baleen introduces a per-hop condensed retrievalpipeline to mitigate the size of the search space, a focused late interaction retriever (FliBERT) that can model complex multi-hop queries, and a weak supervision strategy, latent hop ordering, a weak-supervision strategy in which the trained retriever itself selects the sequence of hops .
Edit intent: coherence
Original:  We explore the more realistic few-shot setting, where only a few hundred training examples are available . We show that standard span selection models perform poorly, highlighting the fact that current pretraining objective are far removed from question answering.
Revised:  We explore the more realistic few-shot setting, where only a few hundred training examples are available , and observe that standard models perform poorly, highlighting the fact that current pretraining objective are far removed from question answering.
Edit intent: clarity
Original:  To address this, we propose a new pretraining scheme that is more suitable for extractive question answering . Given a passage with multiple sets of recurring spans, we mask in each set all recurring spans but one, and ask the model to select the correct span in the passage for each masked span.
Revised:  We propose a new pretraining scheme that is more suitable for extractive question answering . Given a passage with multiple sets of recurring spans, we mask in each set all recurring spans but one, and ask the model to select the correct span in the passage for each masked span.
Edit intent: coherence
Original:  To address this, we propose a new pretraining scheme that is more suitable for extractive question answering . Given a passage with multiple sets of recurring spans, we mask in each set all recurring spans but one, and ask the model to select the correct span in the passage for each masked span.
Revised:  To address this, we propose a new pretraining scheme tailored for question answering: recurring span selection . Given a passage with multiple sets of recurring spans, we mask in each set all recurring spans but one, and ask the model to select the correct span in the passage for each masked span.
Edit intent: clarity
Original:  The resulting model obtains surprisingly good results on multiple benchmarks , e.g., 72.7 F1  with only 128 examples on SQuAD , while maintaining competitive (and sometimes better) performance in the high-resource setting .
Revised:  The resulting model obtains surprisingly good results on multiple benchmarks ( e.g., 72.7 F1 with only 128 examples on SQuAD , while maintaining competitive (and sometimes better) performance in the high-resource setting .
Edit intent: fluency
Original:  The resulting model obtains surprisingly good results on multiple benchmarks , e.g., 72.7 F1  with only 128 examples on SQuAD , while maintaining competitive (and sometimes better) performance in the high-resource setting .
Revised:  The resulting model obtains surprisingly good results on multiple benchmarks , e.g., 72.7 F1 on SQuAD with only 128 examples on SQuAD , while maintaining competitive (and sometimes better) performance in the high-resource setting .
Edit intent: fluency
Original:  The resulting model obtains surprisingly good results on multiple benchmarks , e.g., 72.7 F1  with only 128 examples on SQuAD , while maintaining competitive (and sometimes better) performance in the high-resource setting .
Revised:  The resulting model obtains surprisingly good results on multiple benchmarks , e.g., 72.7 F1 with only 128 training examples) , while maintaining competitive (and sometimes better) performance in the high-resource setting .
Edit intent: meaning-changed