wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We propose FLIN, a natural language interface for web navigation that maps NL commands to concept-level actions  rather than low-level UI interactions , thus being able to flexibly adapt to different websites and handle their transient nature.
Revised:  We propose FLIN, a natural language interface for web navigation that maps NL commands to concept-level actions rather than low-level UI actions) , thus being able to flexibly adapt to different websites and handle their transient nature.
Edit intent: fluency
Original: Prediction bias in machine learning models refers to unintended model behaviors that discriminate against inputs mentioning or produced by certain groups ; for example, hate speech classifiers predict more false positives for neutral text mentioning specific social groups. Mitigating bias for each task or domain is inefficient, as it requires repetitive model training, data annotation (e.g., demographic information), and evaluation. In pursuit of a more accessible solution, we propose the Upstream Bias Mitigation for Downstream Fine-Tuning (UBM) framework, which mitigate one or multiple bias factors in downstream classifiers by transfer learning from an upstream model .
Revised: Fine-tuned language models have been shown to exhibit biases against protected groups in a host of modeling tasks such as text classification and coreference resolution. Previous works focus on detecting these biases, reducing bias in data representations, and using auxiliary training objectives to mitigate bias during fine-tuning. Although these techniques achieve bias reduction for the task and domain at hand, the effects of bias mitigation may not directly transfer to new tasks, requiring additional data collection and customized annotation of sensitive attributes, and re-evaluation of appropriate fairness metrics. We explore the feasibility and benefits of upstream bias mitigation (UBM) framework, which mitigate one or multiple bias factors in downstream classifiers by transfer learning from an upstream model .
Edit intent: coherence
Original:  In pursuit of a more accessible solution, we propose the Upstream Bias Mitigation for Downstream Fine-Tuning (UBM) framework, which mitigate one or multiple bias factors in downstream classifiers by transfer learning from an upstream model .
Revised:  In pursuit of a more accessible solution, we propose the Upstream Bias Mitigation for Downstream Fine-Tuning (UBM) for reducing bias on downstream tasks, by first applying bias mitigation to an upstream model .
Edit intent: clarity
Original:  In pursuit of a more accessible solution, we propose the Upstream Bias Mitigation for Downstream Fine-Tuning (UBM) framework, which mitigate one or multiple bias factors in downstream classifiers by transfer learning from an upstream model . In the upstream bias mitigation stage, explanation regularization and adversarial training are applied to mitigate multiple bias factors. In the downstream fine-tuning stage, the classifier layer of the model is re-initialized, and the entire model is fine-tuned to downstream tasks in potentially novel domains without any further bias mitigation.
Revised:  In pursuit of a more accessible solution, we propose the Upstream Bias Mitigation for Downstream Fine-Tuning (UBM) framework, which mitigate one or multiple bias factors in downstream classifiers by transfer learning from an upstream model through fine-tuning stage, the classifier layer of the model is re-initialized, and the entire model is fine-tuned to downstream tasks in potentially novel domains without any further bias mitigation.
Edit intent: coherence
Original:  In the downstream fine-tuning stage, the classifier layer of the model is re-initialized, and the entire model is fine-tuned to downstream tasks in potentially novel domains without any further bias mitigation. We expect downstream classifiers to be less biased by transfer learning from de-biased upstream models . We conduct extensive experiments varying the similarity between the source and target data, as well as varying the number of dimensions of bias (e.g., discrimination against specific social groups or dialects). Our results indicate the proposed UBM framework can effectively reduce bias in downstream classifiers .
Revised:  In the downstream fine-tuning and subsequently using it for downstream fine-tuning. We find, in extensive experiments across hate speech detection, toxicity detection, occupation prediction, and coreference resolution tasks over various bias factors, that the effects of UBM are indeed transferable to new downstream tasks or domains via fine-tuning, creating less biased downstream models than directly fine-tuning on the downstream task or transferring from a vanilla upstream model. Though challenges remain, we show that UBM promises more efficient and accessible bias mitigation in LM fine-tuning .
Edit intent: coherence
Original:   In addition, experimental results demonstrate that our multi-task NAT is complementary to  the standard knowledge transfer method , knowledge distillation.
Revised:  Furthermore, the performance on large-scale WMT19 and WMT20 English-German datasets confirm the consistency of our proposed method. In addition, experimental results demonstrate that our multi-task NAT is complementary to the standard knowledge transfer method , knowledge distillation.
Edit intent: meaning-changed
Original: Symbolic knowledge (e.g., entities, relations, and facts in a knowledge graph) has become an increasingly popular component of neural-symbolic models applied to machine learning tasks, such as question answering and recommender systems.
Revised: Symbolic knowledge (e.g., entities, relations, and facts in a knowledge graph) has become an increasingly popular component of neural-symbolic models improve performance on various knowledge-intensive tasks, like question answering and recommender systems.
Edit intent: meaning-changed
Original: Symbolic knowledge (e.g., entities, relations, and facts in a knowledge graph) has become an increasingly popular component of neural-symbolic models applied to machine learning tasks, such as question answering and recommender systems. Besides improving downstream performance, these symbolic structures (and their associated attention weights) are often used to help explain the model's predictions and provide " insights " to practitioners .
Revised: Symbolic knowledge (e.g., entities, relations, and facts in a knowledge graph) has become an increasingly popular component of neural-symbolic models applied to machine learning tasks, such as question answering and item recommendation. By using attention over the KG, such models can also " insights " to practitioners .
Edit intent: clarity
Original:  We demonstrate that, through a learned strategy (or even simple heuristics), one can produce deceptively perturbed symbolic structures which maintain the downstream performance of the original structure while significantly deviating from the original semantics .
Revised:  We demonstrate that, through a reinforcement learning policy (or even simple heuristics), one can produce deceptively perturbed symbolic structures which maintain the downstream performance of the original structure while significantly deviating from the original semantics .
Edit intent: clarity
Original:  We demonstrate that, through a learned strategy (or even simple heuristics), one can produce deceptively perturbed symbolic structures which maintain the downstream performance of the original structure while significantly deviating from the original semantics .
Revised:  We demonstrate that, through a learned strategy (or even simple heuristics), one can produce deceptively perturbed KGs which maintain the downstream performance of the original structure while significantly deviating from the original semantics .
Edit intent: clarity
Original:  We demonstrate that, through a learned strategy (or even simple heuristics), one can produce deceptively perturbed symbolic structures which maintain the downstream performance of the original structure while significantly deviating from the original semantics .
Revised:  We demonstrate that, through a learned strategy (or even simple heuristics), one can produce deceptively perturbed symbolic structures which maintain the downstream performance of the original KG while significantly deviating from the original semantics .
Edit intent: meaning-changed
Original:  We demonstrate that, through a learned strategy (or even simple heuristics), one can produce deceptively perturbed symbolic structures which maintain the downstream performance of the original structure while significantly deviating from the original semantics . In particular, we train a reinforcement learning policy to manipulate relation types or edge connections in a knowledge graph, such that the resulting downstream performance is maximally preserved. Across multiple models and tasks, our approach drastically alters knowledge graphs with little to no drop in performance. These results raise doubts about the faithfulness of explanations provided by learned symbolic structures and the reliability of current neural-symbolic modelsin leveraging symbolic knowledge .
Revised:  We demonstrate that, through a learned strategy (or even simple heuristics), one can produce deceptively perturbed symbolic structures which maintain the downstream performance of the original structure while significantly deviating from the original semantics and structure. Our findings raise doubts about the faithfulness of explanations provided by learned symbolic structures and the reliability of current neural-symbolic modelsin leveraging symbolic knowledge .
Edit intent: clarity
Original:  We demonstrate that, through a reinforcement learning policy (or even simple heuristics), one can produce deceptively perturbed KGs  which maintain the downstream performance of the original KG while significantly deviating from the original  semantics and structure.
Revised:  We show that, through a reinforcement learning policy (or even simple heuristics), one can produce deceptively perturbed KGs which maintain the downstream performance of the original KG while significantly deviating from the original semantics and structure.
Edit intent: clarity
Original:  We demonstrate that, through a reinforcement learning policy (or even simple heuristics), one can produce deceptively perturbed KGs  which maintain the downstream performance of the original KG while significantly deviating from the original  semantics and structure.
Revised:  We demonstrate that, through a reinforcement learning policy (or even simple heuristics), one can produce deceptively perturbed KGs , which maintain the downstream performance of the original KG while significantly deviating from the original semantics and structure.
Edit intent: fluency
Original:  We demonstrate that, through a reinforcement learning policy (or even simple heuristics), one can produce deceptively perturbed KGs  which maintain the downstream performance of the original KG while significantly deviating from the original  semantics and structure.
Revised:  We demonstrate that, through a reinforcement learning policy (or even simple heuristics), one can produce deceptively perturbed KGs which maintain the downstream performance of the original KG while significantly deviating from the original KG's semantics and structure.
Edit intent: clarity
Original: Recently, neural-symbolic architectures have achieved success on commonsense reasoning through effectively encoding relational structures retrieved from external knowledge graphs (KGs) and obtained state-of-the-art results in tasks such as (commonsense ) question answering and natural language inference .
Revised: Recently, neural-symbolic models have achieved noteworthy success in leveraging knowledge graphs (KGs) and obtained state-of-the-art results in tasks such as (commonsense ) question answering and natural language inference .
Edit intent: clarity
Original: Recently, neural-symbolic architectures have achieved success on commonsense reasoning through effectively encoding relational structures retrieved from external knowledge graphs (KGs) and obtained state-of-the-art results in tasks such as (commonsense ) question answering and natural language inference .
Revised: Recently, neural-symbolic architectures have achieved success on commonsense reasoning through effectively encoding relational structures retrieved from external knowledge graphs (KGs) for commonsense reasoning tasks, like question answering (QA) .
Edit intent: clarity
Original:  However, these methods rely on quality and contextualized knowledge structures (i.e., fact triples) that are retrieved at the pre-processing stage but overlook challenges caused by incompleteness of a KG, limited expressiveness of its relations, and retrieved facts irrelevant to the reasoning context. In this paper, we present a novel neural-symbolic model, named Hybrid Graph Network (HGN), which jointly generates feature representations for new triples (as a complement to existing edges in the KG) , determines the relevance of the triples to the reasoning context, and learns graph module parameters for encoding the relational information.
Revised:  However, fact sparsity, inherent in human-annotated KGs, can hinder such models from retrieving task-relevant knowledge. To address these issues, we propose Hybrid Graph Network (HGN), which jointly generates feature representations for new triples (as a complement to existing edges in the KG) , determines the relevance of the triples to the reasoning context, and learns graph module parameters for encoding the relational information.
Edit intent: clarity
Original:  In this paper, we present a novel neural-symbolic model, named Hybrid Graph Network (HGN), which jointly generates feature representations for new triples (as a complement to existing edges in the KG) , determines the relevance of the triples to the reasoning context, and learns graph module parameters for encoding the relational information. Our model learns a compact graph structure(comprising both extracted and generated edges) through filtering edges that are unhelpful to the reasoning process. We show marked improvement on three commonsense reasoning benchmarks and demonstrate the superiority of the learned graph structures with user studies .
Revised:  In this paper, we present a novel neural-symbolic model, named Hybrid Graph Network (HGN), a neural-symbolic model that reasons over both extracted (human-labeled) and generated facts within the same learned graph structure. Given a KG subgraph of extracted facts, HGN is jointly trained to generate complementary facts, encode relational information in the resulting "hybrid" subgraph, and filter out task-irrelevant facts. We demonstrate HGN's ability to produce contextually pertinent subgraphs by showing considerable performance gains across four commonsense reasoning benchmarks and demonstrate the superiority of the learned graph structures with user studies .
Edit intent: meaning-changed
Original:  Next to the mechanical components such as strain and displacements, the model accounts for biological parameters such as the concentration of chemokines , the cellular densities of fibroblasts and myofibroblasts, and the density of collagen.
Revised:  Next to the mechanical components such as strain and displacements, the model accounts for biological constituents such as the concentration of chemokines , the cellular densities of fibroblasts and myofibroblasts, and the density of collagen.
Edit intent: clarity
Original:  Next to the mechanical components such as strain and displacements, the model accounts for biological parameters such as the concentration of chemokines , the cellular densities of fibroblasts and myofibroblasts, and the density of collagen.
Revised:  Next to the mechanical components such as strain and displacements, the model accounts for biological parameters such as the concentration of signaling molecules , the cellular densities of fibroblasts and myofibroblasts, and the density of collagen.
Edit intent: clarity
Original:  We present separate stability constraints for the mechanical part of the one-dimensional morphoelastic model and stability constraints for the chemical part of the one-dimensional morphoelastic model. Stability results are developed for both the continuous and (semi-) discrete problems.
Revised:  We present separate stability constraints for the mechanical part of the one-dimensional counterpart of this morphoelastic model, for both the continuous and (semi-) discrete problems.
Edit intent: clarity
Original:  Stability results are developed for both the continuous and (semi-) discrete problems. For the mechanical part, we distinguish between elasticity and plasticity. We perform numerical validation to these constraints and provide a biological interpretation of the (in)stability.
Revised:  Stability results are developed for both the continuous and (semi-) discrete problem. We show that the truncation error between these eigenvalues associated with the continuous and semi-discrete problem is of order \mathcal{O perform numerical validation to these constraints and provide a biological interpretation of the (in)stability.
Edit intent: meaning-changed
Original:   The method leverages (1)  a pre-trained language model (BERT) to adjust for the text , and (2) distant supervision to improve the quality of noisy proxies .
Revised:  Based on these results, we introduce TextCause, an algorithm for estimating causal effects of linguistic properties. The method leverages (1) a pre-trained language model (BERT) to adjust for the text , and (2) distant supervision to improve the quality of noisy proxies .
Edit intent: meaning-changed
Original:   The method leverages (1)  a pre-trained language model (BERT) to adjust for the text , and (2) distant supervision to improve the quality of noisy proxies .
Revised:  The method leverages (1) distant supervision to improve the quality of noisy proxies, and (2) a pre-trained language model (BERT) to adjust for the text , and (2) distant supervision to improve the quality of noisy proxies .
Edit intent: coherence
Original:  We show that our algorithm produces better causal estimates than related methods on two datasets: predicting the effect of music review sentiment on sales, and complaint politeness on response time .
Revised:  We show that our algorithm produces better causal estimates than related methods on two datasets: predicting the effect of music review sentiment on semi-simulated sales figures. Finally, we present an applied case study investigating the effects of complaint politeness on response time .
Edit intent: meaning-changed
Original:  A third  approach is to 'shrink and fine-tune' (SFT), which avoids any explicit distillation by transferring parameters to a  student model and then fine-tuning.
Revised:  A third , simpler approach is to 'shrink and fine-tune' (SFT), which avoids any explicit distillation by transferring parameters to a student model and then fine-tuning.
Edit intent: meaning-changed
Original:  A third  approach is to 'shrink and fine-tune' (SFT), which avoids any explicit distillation by transferring parameters to a  student model and then fine-tuning.
Revised:  A third approach is to 'shrink and fine-tune' (SFT), which avoids any explicit distillation by transferring parameters to a smaller student model and then fine-tuning.
Edit intent: meaning-changed
Original:  This work considers distillation of BART and Pegasus , two state of the art  summarization models, on two datasets across a variety of student models. We produce high quality, fast checkpoints across different computational budgets, and learn some patterns about which distillation techniques perform well in which situations. PyTorch code to rerun our methods, and use the distilled BART and Pegasus checkpoints is available in Hugging Face transformers .
Revised:  This work considers distillation of BART and Pegasus , two state of the art summarization models, and find that SFT outperforms knowledge distillation and pseudo-labeling on the CNN/DailyMail dataset, but under-performs pseudo-labeling on the more abstractive XSUM dataset. PyTorch Code and checkpoints of different sizes are available through Hugging Face transformers .
Edit intent: meaning-changed
Original: Common datasets have the form of %DIFDELCMD < {\em %%% elements with%DIFDELCMD < {\em %%% keys (e.g., transactions and products) and the goal is to perform analytics on the aggregated form of %DIFDELCMD < {\em %%% key and%DIFDELCMD < {\em %%% frequency pairs.
Revised: Common datasets have the form of %DIFDELCMD < {\em %%% with%DIFDELCMD < {\em %%% keys (e.g., transactions and products) and the goal is to perform analytics on the aggregated form of %DIFDELCMD < {\em %%% key and%DIFDELCMD < {\em %%% frequency pairs.
Edit intent: clarity
Original: Common datasets have the form of %DIFDELCMD < {\em %%% elements with%DIFDELCMD < {\em %%% keys (e.g., transactions and products) and the goal is to perform analytics on the aggregated form of %DIFDELCMD < {\em %%% key and%DIFDELCMD < {\em %%% frequency pairs.
Revised: Common datasets have the form of %DIFDELCMD < {\em %%% elements %DIFDELCMD < {\em %%% keys (e.g., transactions and products) and the goal is to perform analytics on the aggregated form of %DIFDELCMD < {\em %%% key and%DIFDELCMD < {\em %%% frequency pairs.
Edit intent: fluency
Original: Common datasets have the form of %DIFDELCMD < {\em %%% elements with%DIFDELCMD < {\em %%% keys (e.g., transactions and products) and the goal is to perform analytics on the aggregated form of %DIFDELCMD < {\em %%% key and%DIFDELCMD < {\em %%% frequency pairs.
Revised: Common datasets have the form of %DIFDELCMD < {\em %%% elements with%DIFDELCMD < {\em %%% elements with keys (e.g., transactions and products) and the goal is to perform analytics on the aggregated form of %DIFDELCMD < {\em %%% key and%DIFDELCMD < {\em %%% frequency pairs.
Edit intent: clarity
Original: Common datasets have the form of %DIFDELCMD < {\em %%% elements with%DIFDELCMD < {\em %%% keys (e.g., transactions and products) and the goal is to perform analytics on the aggregated form of %DIFDELCMD < {\em %%% key and%DIFDELCMD < {\em %%% frequency pairs.
Revised: Common datasets have the form of %DIFDELCMD < {\em %%% elements with%DIFDELCMD < {\em %%% keys (e.g., transactions and products) and the goal is to perform analytics on the aggregated form of %DIFDELCMD < {\em %%% and%DIFDELCMD < {\em %%% frequency pairs.
Edit intent: fluency
Original: Common datasets have the form of %DIFDELCMD < {\em %%% elements with%DIFDELCMD < {\em %%% keys (e.g., transactions and products) and the goal is to perform analytics on the aggregated form of %DIFDELCMD < {\em %%% key and%DIFDELCMD < {\em %%% frequency pairs.
Revised: Common datasets have the form of %DIFDELCMD < {\em %%% elements with%DIFDELCMD < {\em %%% keys (e.g., transactions and products) and the goal is to perform analytics on the aggregated form of %DIFDELCMD < {\em %%% key %DIFDELCMD < {\em %%% frequency pairs.
Edit intent: fluency
Original: Common datasets have the form of %DIFDELCMD < {\em %%% elements with%DIFDELCMD < {\em %%% keys (e.g., transactions and products) and the goal is to perform analytics on the aggregated form of %DIFDELCMD < {\em %%% key and%DIFDELCMD < {\em %%% frequency pairs.
Revised: Common datasets have the form of %DIFDELCMD < {\em %%% elements with%DIFDELCMD < {\em %%% keys (e.g., transactions and products) and the goal is to perform analytics on the aggregated form of %DIFDELCMD < {\em %%% key and%DIFDELCMD < {\em %%% key and frequency pairs.
Edit intent: coherence
Original:  PWS maximizes the reporting probabilities of keys and  improves over the state of the art also for the well-studied special case of %DIFDELCMD < {\em %%% private histograms , when no sampling is performed.
Revised:  PWS maximizes the reporting probabilities of keys and estimation quality of a broad family of statistics. PWS improves over the state of the art also for the well-studied special case of %DIFDELCMD < {\em %%% private histograms , when no sampling is performed.
Edit intent: meaning-changed