wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We then show how to improve the efficiency of recurrence methods in transformers, which let models condition on previously processed tokens ( when generating sequences that are larger than the maximal length that the transformer can handle at once ) . Existing methods require computationally expensive relative position embeddings;
Revised:  We then show how to improve the efficiency of recurrence methods in transformers, which let models condition on previously processed tokens ( when generating sequences that are larger than the maximal length that the transformer can handle at once . Existing methods require computationally expensive relative position embeddings;
Edit intent: fluency
Original:  By combining these techniques, we increase training speed by 65\%, make generation nine times faster , and substantially improve perplexity on WikiText-103, without adding any parameters.
Revised:  We show that these recurrent models also benefit from short input lengths. Combining these techniques speeds up training by a factor of 1.65, reduces memory usage , and substantially improve perplexity on WikiText-103, without adding any parameters.
Edit intent: meaning-changed
Original:  To address and combat this, the NLP community has come to rely on a variety of metrics to identify and quantify bias in black-box models , which are used to monitor model behaviour and to guide efforts at debiasing.
Revised:  To address and combat this, the NLP community relies on a variety of metrics to identify and quantify bias in black-box models , which are used to monitor model behaviour and to guide efforts at debiasing.
Edit intent: coherence
Original:  We advise that efforts to debias embedding spaces be always also paired with measurement of downstream model bias, and suggest that that community direct more effort into making downstream measurement simpler and easier .
Revised:  We advise that efforts to debias embedding spaces be always also paired with measurement of downstream model bias, and suggest that that community direct more effort into making downstream measurement more feasible via creation of additional challenge sets and annotated test data. We additionally release code, a new intrinsic metric, and an annotated test set for gender bias for hatespeech .
Edit intent: meaning-changed
Original: For a single server system, Shortest Remaining Processing Time (SRPT) is a size-based policy that is optimal in the sense that, regardless of the job size distribution, it minimizes the number of jobs in the system at each point in time.
Revised: For a single server system, Shortest Remaining Processing Time (SRPT) is an optimal size-based policy that is optimal in the sense that, regardless of the job size distribution, it minimizes the number of jobs in the system at each point in time.
Edit intent: clarity
Original: For a single server system, Shortest Remaining Processing Time (SRPT) is a size-based policy that is optimal in the sense that, regardless of the job size distribution, it minimizes the number of jobs in the system at each point in time. However, one reason that size-based policies such as SRPT are rarely deployed in practice is that the exact processing times of jobs are usually not known to the scheduler.
Revised: For a single server system, Shortest Remaining Processing Time (SRPT) is a size-based policy .
Edit intent: coherence
Original:  When the estimation error distribution is known, the Gittins' Index policy is known to be optimal in minimizing the mean sojourn time in an M/G/1 queue. For a multiplicative error model, we first characterize the Gittins' Index policy for any estimation error distribution. We then use insights from the Gittins' Index policy to construct a simple heuristic, Size Estimate Hedging (SEH), that only uses jobs' estimated processing times for scheduling while exhibiting near-optimal performance .
Revised:  We propose a simple heuristic, Size Estimate Hedging (SEH), that only uses jobs' estimated processing times for scheduling while exhibiting near-optimal performance .
Edit intent: clarity
Original:  We then use insights from the Gittins' Index policy to construct a simple heuristic, Size Estimate Hedging (SEH), that only uses jobs' estimated processing times for scheduling while exhibiting near-optimal performance .
Revised:  We then use insights from the Gittins' Index policy to construct a simple heuristic, Size Estimate Hedging (SEH), that only uses jobs' estimated processing times for scheduling decisions. A job's priority is increased dynamically according to an SRPT rule until it is determined that it is underestimated, at which time the priority is frozen. Numerical results suggest that SEH has desirable performance when estimation errors are not unreasonably large .
Edit intent: meaning-changed
Original:  Here, we combine these two biophysical methodologies in a single home-made instrument to enable the simultaneous detection of orthogonal fluorescence polarization signals from single fluorescent protein molecules used as common reporters on the localization on biomolecules in cellular processes , whose spatial location can be pinpointed to a super-resolved precision better than the diffraction-limited optical resolution  .
Revised:  Here, we combine these two biophysical methodologies in a single home-made instrument to enable the simultaneous detection of orthogonal fluorescence polarization signals from single fluorescent protein molecules used as common reporters on the localization of proteins in cellular processes , whose spatial location can be pinpointed to a super-resolved precision better than the diffraction-limited optical resolution .
Edit intent: clarity
Original:  Here, we combine these two biophysical methodologies in a single home-made instrument to enable the simultaneous detection of orthogonal fluorescence polarization signals from single fluorescent protein molecules used as common reporters on the localization on biomolecules in cellular processes , whose spatial location can be pinpointed to a super-resolved precision better than the diffraction-limited optical resolution  .
Revised:  Here, we combine these two biophysical methodologies in a single home-made instrument to enable the simultaneous detection of orthogonal fluorescence polarization signals from single fluorescent protein molecules used as common reporters on the localization on biomolecules in cellular processes . These enable measurement of spatial location to a super-resolved precision better than the diffraction-limited optical resolution .
Edit intent: clarity
Original:  Here, we combine these two biophysical methodologies in a single home-made instrument to enable the simultaneous detection of orthogonal fluorescence polarization signals from single fluorescent protein molecules used as common reporters on the localization on biomolecules in cellular processes , whose spatial location can be pinpointed to a super-resolved precision better than the diffraction-limited optical resolution  .
Revised:  Here, we combine these two biophysical methodologies in a single home-made instrument to enable the simultaneous detection of orthogonal fluorescence polarization signals from single fluorescent protein molecules used as common reporters on the localization on biomolecules in cellular processes , whose spatial location can be pinpointed to a super-resolved precision better than the diffraction-limited optical resolution , as well as estimation of molecular stoichiometry based on the brightness of individual fluorophores .
Edit intent: meaning-changed
Original:  In this small innovation we have adapted a millisecond timescale "Slimfield" microscope used for single-molecule detection to enable spitting of the fluorescence polarization emissions into two separate imaging channels for s- and p- polarization signals that are imaged onto separate halves of the same high sensitivity back-illuminated CMOS camera detector.
Revised:  In this innovation we have adapted a millisecond timescale "Slimfield" microscope used for single-molecule detection to enable spitting of the fluorescence polarization emissions into two separate imaging channels for s- and p- polarization signals that are imaged onto separate halves of the same high sensitivity back-illuminated CMOS camera detector.
Edit intent: coherence
Original:  In this small innovation we have adapted a millisecond timescale "Slimfield" microscope used for single-molecule detection to enable spitting of the fluorescence polarization emissions into two separate imaging channels for s- and p- polarization signals that are imaged onto separate halves of the same high sensitivity back-illuminated CMOS camera detector.
Revised:  In this small innovation we have adapted a millisecond timescale microscope used for single-molecule detection to enable spitting of the fluorescence polarization emissions into two separate imaging channels for s- and p- polarization signals that are imaged onto separate halves of the same high sensitivity back-illuminated CMOS camera detector.
Edit intent: coherence
Original:  In this small innovation we have adapted a millisecond timescale "Slimfield" microscope used for single-molecule detection to enable spitting of the fluorescence polarization emissions into two separate imaging channels for s- and p- polarization signals that are imaged onto separate halves of the same high sensitivity back-illuminated CMOS camera detector.
Revised:  In this small innovation we have adapted a millisecond timescale "Slimfield" microscope used for single-molecule detection to enable splitting of fluorescence polarization emissions into two separate imaging channels for s- and p- polarization signals that are imaged onto separate halves of the same high sensitivity back-illuminated CMOS camera detector.
Edit intent: clarity
Original:  In this small innovation we have adapted a millisecond timescale "Slimfield" microscope used for single-molecule detection to enable spitting of the fluorescence polarization emissions into two separate imaging channels for s- and p- polarization signals that are imaged onto separate halves of the same high sensitivity back-illuminated CMOS camera detector.
Revised:  In this small innovation we have adapted a millisecond timescale "Slimfield" microscope used for single-molecule detection to enable spitting of the fluorescence polarization emissions into two separate imaging channels for s- and p- polarization signals , which are imaged onto separate halves of the same high sensitivity back-illuminated CMOS camera detector.
Edit intent: fluency
Original:  We applied this fluorescence polarization super-resolved imaging modality to a range of test fluorescent samples relevant to the study of biological processes, including purified monomeric green fluorescent protein (mGFP) .
Revised:  We applied this fluorescence polarization super-resolved imaging modality to a range of test fluorescent samples relevant to the study of biological processes, including purified monomeric green fluorescent protein , single combed DNA molecules, and protein assemblies and complexes from live Escherichia coli and Saccharomyces cerevisiae cells .
Edit intent: meaning-changed
Original:  Our findings are largely qualitative but demonstrate promise in showing how fluorescence polarization and Slimfield-mediated super-resolved localization microscopy can be combined on the same sample to enable new biological insights .
Revised:  Our findings are largely qualitative but demonstrate promise in showing how fluorescence polarization and Slimfield-mediated super-resolved localization microscopy can be combined on the same sample to enable simultaneous measurements of polarization and stoichiometry of tracked molecular complexes, as well as the translational diffusion coefficient .
Edit intent: meaning-changed
Original: The Dual Matrix Algorithm  , variations of which were proposed in A.
Revised: NP (search) problems allow easy correctness tests for solutions. Climbing algorithms allow also easy assessment of how close to yielding the correct answer is the configuration at any stage of their run. This offers a great flexibility, as how sensible is any deviation from the standard procedures can be instantly assessed. An example is the Dual Matrix Algorithm , variations of which were proposed in A.
Edit intent: meaning-changed
Original: The Dual Matrix Algorithm  , variations of which were proposed in A.Yu. Levin 65%DIFDELCMD < ] %%% and Yamnitsky , Levin 82%DIFDELCMD < ]%%% , has little sensitivity to numerical errors and to the number of inequalities.
Revised: The Dual Matrix Algorithm , variations of which were proposed in %DIFDELCMD < ] %%% and Yamnitsky , Levin 82%DIFDELCMD < ]%%% , has little sensitivity to numerical errors and to the number of inequalities.
Edit intent: clarity
Original: Yu. Levin 65%DIFDELCMD < ] %%% and Yamnitsky , Levin 82%DIFDELCMD < ]%%% , has little sensitivity to numerical errors and to the number of inequalities.
Revised: Yu. Levin 65%DIFDELCMD < ] %%% Yamnitsky , Levin 82%DIFDELCMD < ]%%% , has little sensitivity to numerical errors and to the number of inequalities.
Edit intent: coherence
Original: Yu. Levin 65%DIFDELCMD < ] %%% and Yamnitsky , Levin 82%DIFDELCMD < ]%%% , has little sensitivity to numerical errors and to the number of inequalities.
Revised: Yu. Levin 65%DIFDELCMD < ] %%% and %DIFDELCMD < ]%%% , has little sensitivity to numerical errors and to the number of inequalities.
Edit intent: clarity
Original: Yu. Levin 65%DIFDELCMD < ] %%% and Yamnitsky , Levin 82%DIFDELCMD < ]%%% , has little sensitivity to numerical errors and to the number of inequalities.
Revised: Yu. Levin 65%DIFDELCMD < ] %%% and Yamnitsky , Levin 82%DIFDELCMD < ]%%% considered by A.Y. Levin in 1965 and by Yamnitsky and myself in 1982. It has little sensitivity to numerical errors and to the number of inequalities.
Edit intent: meaning-changed
Original:  While fine-tuning large language models usually gives the best performance, in many applications it is preferable to tune much smaller sets of parameters, so that the majority of parameters can be shared across multiple tasks . The main approach is to train one or more task-specific layers on top of the language model.
Revised:  A common approach to transfer learning for multiple tasks that maximize parameter sharing trains one or more task-specific layers on top of the language model.
Edit intent: clarity
Original:  We show that this approach outperforms other methods with a similar number of trainable parameters on SST-2 and MNLI datasets. On SST-2, the performance of our model is comparable to the fully fine-tuned baseline, while on MNLI it is the best among the methods that do not modify the parameters of the body of the language model .
Revised:  We show that this approach outperforms other methods with a similar number of trainable parameters on the public leaderboard of the GLUE benchmark. Our method, initialized with task-specific human-readable prompts, also works in a few-shot setting, outperforming GPT-3 on two SuperGLUE tasks with just 32 training samples .
Edit intent: meaning-changed
Original:  We present two alternative neural approaches as baselines : (1) formulating intent classification and slot filling as a joint sequence tagging and (2) modeling them as a sequence-to-sequence (Seq2Seq) learning task.
Revised:  We present two alternative neural approaches as baselines , (1) formulating intent classification and slot filling as a joint sequence tagging and (2) modeling them as a sequence-to-sequence (Seq2Seq) learning task.
Edit intent: fluency
Original:  We present two alternative neural approaches as baselines : (1) formulating intent classification and slot filling as a joint sequence tagging and (2) modeling them as a sequence-to-sequence (Seq2Seq) learning task.
Revised:  We present two alternative neural approaches as baselines : (1) intent classification and slot filling as a joint sequence tagging and (2) modeling them as a sequence-to-sequence (Seq2Seq) learning task.
Edit intent: clarity
Original:  To address the limitations of previous approaches, in this paper, we propose an alternative commonsense knowledge acquisition framework DISCOS (from DIScourse to COmmonSense), which automatically mines expensive complex commonsense knowledge from more affordable linguistic knowledge resources.
Revised:  To address the limitations of previous approaches, in this paper, we propose an alternative commonsense knowledge acquisition framework DISCOS (from DIScourse to COmmonSense), which automatically populates expensive complex commonsense knowledge from more affordable linguistic knowledge resources.
Edit intent: clarity
Original:  To address the limitations of previous approaches, in this paper, we propose an alternative commonsense knowledge acquisition framework DISCOS (from DIScourse to COmmonSense), which automatically mines expensive complex commonsense knowledge from more affordable linguistic knowledge resources.
Revised:  To address the limitations of previous approaches, in this paper, we propose an alternative commonsense knowledge acquisition framework DISCOS (from DIScourse to COmmonSense), which automatically mines expensive complex commonsense knowledge to more affordable linguistic knowledge resources.
Edit intent: fluency
Original:  Experiments demonstrate that we can successfully convert discourse knowledge over eventualities from ASER, a large-scale discourse knowledge graph, into inferential if-then commonsense knowledge defined in ATOMIC without any additional annotation effort.
Revised:  Experiments demonstrate that we can successfully convert discourse knowledge about eventualities from ASER, a large-scale discourse knowledge graph, into inferential if-then commonsense knowledge defined in ATOMIC without any additional annotation effort.
Edit intent: fluency
Original:  Experiments demonstrate that we can successfully convert discourse knowledge over eventualities from ASER, a large-scale discourse knowledge graph, into inferential if-then commonsense knowledge defined in ATOMIC without any additional annotation effort.
Revised:  Experiments demonstrate that we can successfully convert discourse knowledge over eventualities from ASER, a large-scale discourse knowledge graph, into if-then commonsense knowledge defined in ATOMIC without any additional annotation effort.
Edit intent: clarity
Original:  By exploiting a relatively large monolingual corpus of the target programming language, which is cheap to mine from the web, unlike a parallel corpus, we achieved 80.75 \% exact match accuracy on Django and 32.57 BLEU score on CoNaLa , both are SOTA to the best of our knowledge.
Revised:  By exploiting a relatively sizeable monolingual corpus of the target programming language, which is cheap to mine from the web, unlike a parallel corpus, we achieved 80.75 \% exact match accuracy on Django and 32.57 BLEU score on CoNaLa , both are SOTA to the best of our knowledge.
Edit intent: clarity
Original:  By exploiting a relatively large monolingual corpus of the target programming language, which is cheap to mine from the web, unlike a parallel corpus, we achieved 80.75 \% exact match accuracy on Django and 32.57 BLEU score on CoNaLa , both are SOTA to the best of our knowledge.
Revised:  By exploiting a relatively large monolingual corpus of the target programming language, which is cheap to mine from the web, we achieved 81.03 \% exact match accuracy on Django and 32.57 BLEU score on CoNaLa , both are SOTA to the best of our knowledge.
Edit intent: meaning-changed
Original:  By exploiting a relatively large monolingual corpus of the target programming language, which is cheap to mine from the web, unlike a parallel corpus, we achieved 80.75 \% exact match accuracy on Django and 32.57 BLEU score on CoNaLa , both are SOTA to the best of our knowledge.
Revised:  By exploiting a relatively large monolingual corpus of the target programming language, which is cheap to mine from the web, unlike a parallel corpus, we achieved 80.75 \% exact match accuracy on Django and 32.57 BLEU score on CoNaLa . Both are SOTA to the best of our knowledge.
Edit intent: fluency
Original:  It also shows substantial retrieving speed advantages, i.e. for an index with 1 million images, VisualSparta gets over 391x speed up compared to standard vector search . Experiments show that this speed advantage even gets bigger for larger datasets because VisualSparta can be efficiently implemented as an inverted index.
Revised:  We also show that it achieves substantial retrieving speed advantages, i.e. for an index with 1 million images, VisualSparta gets over 391x speed up compared to standard vector search . Experiments show that this speed advantage even gets bigger for larger datasets because VisualSparta can be efficiently implemented as an inverted index.
Edit intent: clarity
Original:  It also shows substantial retrieving speed advantages, i.e. for an index with 1 million images, VisualSparta gets over 391x speed up compared to standard vector search . Experiments show that this speed advantage even gets bigger for larger datasets because VisualSparta can be efficiently implemented as an inverted index.
Revised:  It also shows substantial retrieving speed advantages, i.e. , for a 1 million images, VisualSparta gets over 391x speed up compared to standard vector search . Experiments show that this speed advantage even gets bigger for larger datasets because VisualSparta can be efficiently implemented as an inverted index.
Edit intent: clarity
Original:  It also shows substantial retrieving speed advantages, i.e. for an index with 1 million images, VisualSparta gets over 391x speed up compared to standard vector search . Experiments show that this speed advantage even gets bigger for larger datasets because VisualSparta can be efficiently implemented as an inverted index.
Revised:  It also shows substantial retrieving speed advantages, i.e. for an index with 1 million image index, VisualSparta using CPU gets ~391X speedup compared to CPU vector search and ~5.4X speedup compared to vector search with GPU acceleration . Experiments show that this speed advantage even gets bigger for larger datasets because VisualSparta can be efficiently implemented as an inverted index.
Edit intent: meaning-changed
Original:  To the best of our knowledge, VisualSparta is the first transformer-based text-to-image retrieval model that can achieve real-time searching for very large dataset , with significant accuracy improvement compared to previous state-of-the-art methods.
Revised:  To the best of our knowledge, VisualSparta is the first transformer-based text-to-image retrieval model that can achieve real-time searching for large-scale datasets , with significant accuracy improvement compared to previous state-of-the-art methods.
Edit intent: clarity
Original: Counterfactual examples have been shown to be useful for many applications, including calibrating, evaluating, and explaining model decision boundaries. However, previous methods for generating such counterfactual examples have been tightly tailored to a specific application, used a limited range of linguistic patterns, or are hard to scale. We propose to disentangle counterfactual generation from its use cases, i.e., gather general-purpose counterfactuals first, and then select them for specific applications.
Revised: While counterfactual examples are useful for analysis and training of NLP models, current generation methods either rely on manual labor to create very few counterfactuals, or only instantiate limited types of perturbations such as paraphrases or word substitutions. We present Polyjuice, a general-purpose counterfactuals first, and then select them for specific applications.
Edit intent: coherence