wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Furthermore, we introduce inter-cellular message passing schemes, message passing schemes on cell complexes that take the topology of the underlying space into account .
Revised:  Furthermore, we introduce inter-cellular message passing schemes, message passing schemes on cell complexes that take the topology of the underlying space into account and generalizes message passing scheme to graphs. Finally, we introduce a unified cell complex encoder-decoder framework that enables learning representation of cells for a given complex inside the Euclidean spaces.
Edit intent: meaning-changed
Original:  Overall, our learned closed-loop policy achieves over 90 \% success rates on grasping various ShapeNet objects and YCB objects in the simulation. Our video can be found at URL . \\%DIF > success rates on grasping various ShapeNet objects and YCB objects in simulation.
Revised:  Overall, our learned closed-loop policy achieves over 90 \\%DIF > success rates on grasping various ShapeNet objects and YCB objects in simulation.
Edit intent: coherence
Original:  Previous solutions based on 6D grasp synthesis with robot motion planning usually operate in an open-loop setting without considering perception feedback and dynamicsand contacts of objects, which makes them sensitive to grasp synthesis errors.
Revised:  Previous solutions based on 6D grasp synthesis with robot motion planning usually operate in an open-loop setting without considering perception feedback , dynamics, and contacts with objects, which makes them sensitive to grasp synthesis errors.
Edit intent: clarity
Original:  In this work, we propose a novel method for learning closed-loop control policies for 6D robotic graspingusing point clouds from an egocentric camera  .
Revised:  In this work, we propose a novel method for learning closed-loop control policies for 6D robotic graspingusing point clouds from an egocentric camera as input, and outputs continuous 6D control actions of the robot gripper for grasping the object .
Edit intent: meaning-changed
Original:  We combine imitation learning and reinforcement learning in order to grasp unseen objects and handle the continuous 6D action space, where expert demonstrations are obtained from a joint motion and grasp planner. We introduce a goal-auxiliary actor-critic algorithm , which uses grasping goal prediction as an auxiliary task to facilitate policy learning.
Revised:  We combine imitation learning and reinforcement learning and introduce a goal-auxiliary actor-critic algorithm , which uses grasping goal prediction as an auxiliary task to facilitate policy learning.
Edit intent: coherence
Original:  The supervision on grasping goals can be obtained from the expert planner for known objects or from hindsight goals for unknown objects. Overall, our learned closed-loop policy achieves over 90\% success rates on grasping various ShapeNet objects and YCB objects in simulation. The policy also transfers well to the real world for grasping unseen objects in both a tabletop setting and a human-robot handover setting in our experiments.
Revised:  We demonstrate that our learned policy can be integrated into a tabletop 6D grasping system and a human-robot handover setting in our experiments.
Edit intent: clarity
Original:  The policy also transfers well to the real world for grasping unseen objects in both a tabletop setting and a human-robot handover setting in our experiments. Our video can be found at URL .
Revised:  The policy also transfers well to the real world for grasping unseen objects in both a tabletop setting and a human-robot handover system to improve the grasping performance of unseen objects. Our videos and code can be found at URL .
Edit intent: meaning-changed
Original:  We propose GENRE, the first system that retrieves entities by generating their unique names, left to right, token-by-token in an autoregressive fashion , and conditioned on the context. This enables to mitigate the aforementioned technical issues : i) the autoregressive formulation allows us to directly capture relations between context and entity name, effectively cross encoding both;
Revised:  We propose GENRE, the first system that retrieves entities by generating their unique names, left to right, token-by-token in an autoregressive fashion . This mitigates the aforementioned technical issues : i) the autoregressive formulation allows us to directly capture relations between context and entity name, effectively cross encoding both;
Edit intent: coherence
Original:  We show the efficacy of the approach with more than 20 datasets on entity disambiguation, end-to-end entity linking and document retrieval tasks, achieving new SOTA, or very competitive results while using a tiny fraction of the memory  of competing systems.
Revised:  We experiment with more than 20 datasets on entity disambiguation, end-to-end entity linking and document retrieval tasks, achieving new SOTA, or very competitive results while using a tiny fraction of the memory of competing systems.
Edit intent: clarity
Original:  We show the efficacy of the approach with more than 20 datasets on entity disambiguation, end-to-end entity linking and document retrieval tasks, achieving new SOTA, or very competitive results while using a tiny fraction of the memory  of competing systems.
Revised:  We show the efficacy of the approach with more than 20 datasets on entity disambiguation, end-to-end entity linking and document retrieval tasks, achieving new state-of-the-art or very competitive results while using a tiny fraction of the memory of competing systems.
Edit intent: clarity
Original:  We show the efficacy of the approach with more than 20 datasets on entity disambiguation, end-to-end entity linking and document retrieval tasks, achieving new SOTA, or very competitive results while using a tiny fraction of the memory  of competing systems.
Revised:  We show the efficacy of the approach with more than 20 datasets on entity disambiguation, end-to-end entity linking and document retrieval tasks, achieving new SOTA, or very competitive results while using a tiny fraction of the memory footprint of competing systems.
Edit intent: clarity
Original:  Loosely speaking, this technique allows us to privately test whether the value of a given query  is close to what we expect it would be(w. r.t. the input database), where we are allowed to test an unbounded number of queries as long as their value is indeed close to what we expected.
Revised:  This simple algorithm privately tests whether the value of a given query is close to what we expect it would be(w. r.t. the input database), where we are allowed to test an unbounded number of queries as long as their value is indeed close to what we expected.
Edit intent: clarity
Original:  Loosely speaking, this technique allows us to privately test whether the value of a given query  is close to what we expect it would be(w. r.t. the input database), where we are allowed to test an unbounded number of queries as long as their value is indeed close to what we expected.
Revised:  Loosely speaking, this technique allows us to privately test whether the value of a given query on a database is close to what we expect it would be(w. r.t. the input database), where we are allowed to test an unbounded number of queries as long as their value is indeed close to what we expected.
Edit intent: clarity
Original:  Loosely speaking, this technique allows us to privately test whether the value of a given query  is close to what we expect it would be(w. r.t. the input database), where we are allowed to test an unbounded number of queries as long as their value is indeed close to what we expected.
Revised:  Loosely speaking, this technique allows us to privately test whether the value of a given query is close to what we expect it to be. It allows to ask an unbounded number of queries as long as their value is indeed close to what we expected.
Edit intent: coherence
Original:  Loosely speaking, this technique allows us to privately test whether the value of a given query  is close to what we expect it would be(w. r.t. the input database), where we are allowed to test an unbounded number of queries as long as their value is indeed close to what we expected.
Revised:  Loosely speaking, this technique allows us to privately test whether the value of a given query is close to what we expect it would be(w. r.t. the input database), where we are allowed to test an unbounded number of queries as long as the answer is close to what we expected.
Edit intent: clarity
Original:  Loosely speaking, this technique allows us to privately test whether the value of a given query  is close to what we expect it would be(w. r.t. the input database), where we are allowed to test an unbounded number of queries as long as their value is indeed close to what we expected. After the first time in which this is not the case , the process halts.
Revised:  Loosely speaking, this technique allows us to privately test whether the value of a given query is close to what we expect it would be(w. r.t. the input database), where we are allowed to test an unbounded number of queries as long as their value is indeed close to what we expect, and halts following the first query for which this is not the case , the process halts.
Edit intent: clarity
Original:  As a result, in some cases we are able to continue with the process of testing queries even after the first time in which the value of the query did not meet our expectations .
Revised:  We suggest an alternative, equally simple, algorithm that can continue testing queries as long as any single individual does not contribute to the answer of too many queries whose answer deviates substantially form what we expect. Our analysis is subtle and some of its ingredients may be more widely applicable. In some cases our new algorithm allows to privately extract much more information from the database than the original .
Edit intent: meaning-changed
Original:  We demonstrate our technique by applying it to the shifting-heavy-hitters problem: On every time step, each of n users gets a new input, and the task is to privately identify all the current heavy-hitters.
Revised:  We demonstrate this by applying our algorithm to the shifting heavy-hitters problem: On every time step, each of n users gets a new input, and the task is to privately identify all the current heavy-hitters.
Edit intent: clarity
Original:  Specifically, the error of our algorithm depends on the maximal number of times that a singe user holds a heavy-hitter as input, rather than the total number of times in which a heavy-hitter exists.
Revised:  Specifically, the error of our algorithm depends on the maximal number of times that a single user holds a heavy-hitter as input, rather than the total number of times in which a heavy-hitter exists.
Edit intent: fluency
Original:  First, we show that a (1 + 1{t})-approximation of the optimal STT for a given search distribution can be computed in time O(n^{2t + 1}) for all integers t \geq 1. Second, we identify a broad family of STTs with linear rotation-distance, allowing the generalization of Splay trees to the STT setting.
Revised:  First, we show that a (1 + 1{t})-approximation of an optimal size-n STT for a given search distribution can be computed in time O(n^{2t + 1}) for all integers t \geq 1. Second, we identify a broad family of STTs with linear rotation-distance, allowing the generalization of Splay trees to the STT setting.
Edit intent: meaning-changed
Original:  We show that our generalized Splay satisfies a static optimality theorem, asymptotically matching the cost of the optimal STT in an online fashion, i.e. without a priori knowledge of the search distribution.
Revised:  We show that our generalized Splay satisfies a static optimality theorem, asymptotically matching the cost of the optimal STT in an online fashion, i.e. without knowledge of the search distribution.
Edit intent: clarity
Original: For natural language processing (NLP) taskssuch as sentiment or topic classification, currently prevailing approaches heavily rely on pretraining large self-supervised models on massive external data resources. However, this methodology is being critiqued for: exceptional compute and pretraining data requirements ;
Revised: For natural language processing (NLP) taskssuch as sentiment or topic classification, currently prevailing approaches heavily rely on pretraining large self-supervised models on massive external data sources, which incurs exceptional pretraining data requirements ;
Edit intent: clarity
Original:  However, this methodology is being critiqued for: exceptional compute and pretraining data requirements ; diminishing returns on both large and small datasets; and importantly, favourable evaluation settings that overestimate performance differences. The core belief behind current methodology, coined `the bitter lesson' by R. Sutton, is that `compute scale-up beats data and compute-efficient algorithms', neglecting that progress in compute hardware scale-up is based almost entirely on the miniaturisation of resource consumption. We thus approach pretrainingfrom a miniaturisation perspective, such as not to require massive external data sources and models, or learned translations from continuous input embeddings to discrete labels. To minimise overly favourable evaluation, we examine learning on a long-tailed, low-resource, multi-label text classification dataset with noisy, highly sparse labels and many rare concepts.
Revised:  However, this methodology is being critiqued for: exceptional compute and pretraining data requirements and a diminished ability to pretrain over small datasets. However, fundamental pretraining method capabilities like few to zero-shot learning or preserving minority concept (long-tail) prediction performance along with accordingly designed evaluation scenarios remain open challenges. We thus propose Contrastive Label-Embedding Self-Supervision (CLESS) pretraining, which enables pretraining from multiple magnitudes smaller, 'task internal' data only, while still strongly improving fully supervised, long-tail, few-shot and self-supervised zero-shot learning abilities. Accordingly, we analyse improvements in learning dynamics over baselines on a challenging long-tailed, low-resource, multi-label text classification dataset with noisy, highly sparse labels and many rare concepts.
Edit intent: meaning-changed
Original:  To minimise overly favourable evaluation, we examine learning on a long-tailed, low-resource, multi-label text classification dataset with noisy, highly sparse labels and many rare concepts. To this end, we propose a novel `dataset-internal' contrastive autoencoding approach to self-supervised pretraining and demonstrate marked improvements in zero-shot, few-shot and solely supervised learning performance; even under an unfavorable low-resource scenario, and without defaulting to large-scale external datasets for self-supervision. We also find empirical evidence that zero and few-shot learning markedly benefit from adding more ` dataset-internal' , self-supervised training signals, which is of practical importance when retrieving or computing on large external sources of such signals is infeasible .
Revised:  To minimise overly favourable evaluation, we examine learning on a long-tailed, low-resource, multi-label text classification dataset with noisy, highly sparse labels and many minority concepts. We find that long-tailed zero and few-shot learning markedly benefit from adding more ` dataset-internal' , self-supervised training signals, which is of practical importance when retrieving or computing on large external sources of such signals is infeasible .
Edit intent: coherence
Original:  We also find empirical evidence that zero and few-shot learning markedly benefit from adding more ` dataset-internal' , self-supervised training signals, which is of practical importance when retrieving or computing on large external sources of such signals is infeasible .
Revised:  We also find empirical evidence that zero and few-shot learning markedly benefit from increasing ' dataset-internal' , self-supervised training signals, which is of practical importance when retrieving or computing on large external sources of such signals is infeasible .
Edit intent: clarity
Original:  We also find empirical evidence that zero and few-shot learning markedly benefit from adding more ` dataset-internal' , self-supervised training signals, which is of practical importance when retrieving or computing on large external sources of such signals is infeasible .
Revised:  We also find empirical evidence that zero and few-shot learning markedly benefit from adding more ` dataset-internal' self-supervised training signals, which is of practical importance when retrieving or computing on large external sources of such signals is infeasible .
Edit intent: fluency
Original:  We also find empirical evidence that zero and few-shot learning markedly benefit from adding more ` dataset-internal' , self-supervised training signals, which is of practical importance when retrieving or computing on large external sources of such signals is infeasible .
Revised:  We also find empirical evidence that zero and few-shot learning markedly benefit from adding more ` dataset-internal' , self-supervised pretraining signals, to help reduce the reliance on large external sources of such signals is infeasible .
Edit intent: clarity
Original:  We also find empirical evidence that zero and few-shot learning markedly benefit from adding more ` dataset-internal' , self-supervised training signals, which is of practical importance when retrieving or computing on large external sources of such signals is infeasible .
Revised:  We also find empirical evidence that zero and few-shot learning markedly benefit from adding more ` dataset-internal' , self-supervised training signals, which is of practical importance when retrieving or computing on large external sources .
Edit intent: clarity
Original: For natural language processing ' text-to-text' tasks, the prevailing approaches heavily rely on pretraining large self-supervised models on massive external datasources, which incurs exceptional pretraining data requirements and a diminished ability to pretrain over small datasets.
Revised: For natural language processing ` text-to-text' tasks, the prevailing approaches heavily rely on pretraining large self-supervised models on massive external datasources, which incurs exceptional pretraining data requirements and a diminished ability to pretrain over small datasets.
Edit intent: fluency
Original: For natural language processing ' text-to-text' tasks, the prevailing approaches heavily rely on pretraining large self-supervised models on massive external datasources, which incurs exceptional pretraining data requirements and a diminished ability to pretrain over small datasets.
Revised: For natural language processing ' text-to-text' tasks, the prevailing approaches heavily rely on pretraining large self-supervised models on increasingly larger `task-external' data. Transfer learning from high-resource pretraining works well, but research has focused on settings with very large data and compute requirements, while the potential of efficient low-resource learning, without large `task-external' pretraining, remains under-explored. In this work, we evaluate against three core challenges for resource efficient learning. Namely, we analyze: (1) pretraining data requirements and a diminished ability to pretrain over small datasets.
Edit intent: meaning-changed
Original: For natural language processing ' text-to-text' tasks, the prevailing approaches heavily rely on pretraining large self-supervised models on massive external datasources, which incurs exceptional pretraining data requirements and a diminished ability to pretrain over small datasets. However, fundamental pretraining method capabilities like few to zero-shot learningor preserving minority concept ( long-tail ) prediction performance along with accordingly designed evaluation scenarios remain open challenges .
Revised: For natural language processing ' text-to-text' tasks, the prevailing approaches heavily rely on pretraining large self-supervised models on massive external datasources, which incurs exceptional pretraining data (X) efficiency; (2) zero to few-shot label (Y) efficiency; and (3) long-tail ) prediction performance along with accordingly designed evaluation scenarios remain open challenges .
Edit intent: meaning-changed
Original:  However, fundamental pretraining method capabilities like few to zero-shot learningor preserving minority concept ( long-tail ) prediction performance along with accordingly designed evaluation scenarios remain open challenges . We thus propose Contrastive Label-Embedding Self-Supervision (CLESS) pretraining , which enables pretraining from multiple magnitudes smaller, 'task internal' data only, while still strongly improving fully supervised, long-tail , few-shot and self-supervised zero-shot learning abilities.
Revised:  However, fundamental pretraining method capabilities like few to zero-shot learningor preserving minority concept ( long-tail generalization, since long-tail , few-shot and self-supervised zero-shot learning abilities.
Edit intent: clarity
Original:  We thus propose Contrastive Label-Embedding Self-Supervision (CLESS) pretraining , which enables pretraining from multiple magnitudes smaller, 'task internal' data only, while still strongly improving fully supervised, long-tail , few-shot and self-supervised zero-shot learning abilities. Accordingly, we analyse improvements in learning dynamics over baselines on a challenging long-tailed, low-resource, multi-label text classification scenario with noisy, highly sparse labels and many minority concepts .
Revised:  We thus propose Contrastive Label-Embedding Self-Supervision (CLESS) pretraining , which enables pretraining from multiple magnitudes smaller, 'task internal' data only, while still strongly improving fully supervised, long-tail preservation has been linked to algorithmic fairness and because data in the tail is limited by definition. To address these challenges, we propose a data and compute efficient self-supervised, contrastive text encoder, pretrained on 60MB of `task-internal' text data, and compare it to RoBERTa, which was pretrained on 160GB of `task-external' text .
Edit intent: meaning-changed
Original:  In such an environment, laissez-faire may result in a highly unfair and inefficient outcome---myopic firms are reluctant to hire minority workers because the lack of data about minority workers prevents accurate estimation of their performance.
Revised:  In such an environment, laissez-faire may result in a highly unfair and inefficient outcome -- myopic firms are reluctant to hire minority workers because the lack of data about minority workers prevents accurate estimation of their performance.
Edit intent: fluency
Original:  In such an environment, laissez-faire may result in a highly unfair and inefficient outcome -- myopic firms are reluctant to hire minority workers because the lack of data about minority workers prevents accurate estimation of their performance. Consequently, minority groups could be perpetually underestimated -- they are never hired, and therefore, data about them is never accumulated.
Revised:  Laissez-faire causes perpetual underestimation: minority workers are rarely hired, and therefore, data about them is never accumulated.
Edit intent: clarity