wanyu/IteraTeR_v2 huggingface dataset:
Original:  Group communication splits a high-dimensional feature into groups of low-dimensional features and applies a module to capture the inter-group dependency.
Revised:  Group communication splits a high-dimensional feature into groups of low-dimensional features and captures the inter-group dependency.
Edit intent: clarity
Original:  A model can then be applied to the groupsin parallel with a significantly smaller width . A context codec is applied to decrease the length of a sequential feature ,
Revised:  A separation module with a significantly smaller model size can then be applied to the groupsin parallel with a significantly smaller width . A context codec is applied to decrease the length of a sequential feature ,
Edit intent: clarity
Original:  A model can then be applied to the groupsin parallel with a significantly smaller width . A context codec is applied to decrease the length of a sequential feature ,
Revised:  A model can then be shared by all the groups . A context codec is applied to decrease the length of a sequential feature ,
Edit intent: clarity
Original:  where a context encoder compresses the temporal context of local features into a single feature representing the global characteristics of the context, and a context decoder decompresses the transformed global features back to the context features . Experimental results show that  GC3 can achieve on par or better performance than a wide range of baseline architectures with as small as 2.5\% model size  .
Revised:  where a context encoder compresses the temporal context of local features into a single feature representing the global characteristics of the context, and a context decoder decompresses the transformed global features back to the context features . Experimental results show that GC3 can achieve on par or better performance with as small as 2.5\% model size .
Edit intent: clarity
Original:  The results indicate that our method outperforms very competitivebaselines, and can be placed among the state of the art approaches for unconditional generation on QM9 .
Revised:  The results indicate that our method is competitive, and outperforms challenging baselines for unconditional generation on QM9 .
Edit intent: clarity
Original:  The results indicate that our method outperforms very competitivebaselines, and can be placed among the state of the art approaches for unconditional generation on QM9 .
Revised:  The results indicate that our method outperforms very competitivebaselines, and can be placed among the state of the art approaches for unconditional generation .
Edit intent: clarity
Original:  This kind of defects is the major cause of the gap between  as-designed and as-manufactured mechanical behavior of AM products.
Revised:  This kind of defects is the major cause of the gap between the as-designed and as-manufactured mechanical behavior of AM products.
Edit intent: fluency
Original: A key task for speech recognition systems is to reduce the mismatch between the training and evaluation data that is often attributable to speaker differences.
Revised: A key task for speech recognition systems is to reduce the mismatch between training and evaluation data that is often attributable to speaker differences.
Edit intent: fluency
Original:  To this end, speaker adaptation techniques play a vital role to reduce the mismatch.
Revised:  Speaker adaptation techniques play a vital role to reduce the mismatch.
Edit intent: clarity
Original:  In all three Bayesian adaptation methods, deterministic SD parameters are replaced by latent variable posterior distributions to be learned for each speaker, whose parameters are efficiently estimated using a variational inference based approach.
Revised:  In the three methods, deterministic SD parameters are replaced by latent variable posterior distributions to be learned for each speaker, whose parameters are efficiently estimated using a variational inference based approach.
Edit intent: clarity
Original:  In all three Bayesian adaptation methods, deterministic SD parameters are replaced by latent variable posterior distributions to be learned for each speaker, whose parameters are efficiently estimated using a variational inference based approach.
Revised:  In all three Bayesian adaptation methods, deterministic SD parameters are replaced by latent variable posterior distributions for each speaker, whose parameters are efficiently estimated using a variational inference based approach.
Edit intent: coherence
Original:  Experiments conducted on 300-hour speed perturbed Switchboard corpus trained LF-MMI factored TDNN/CNN-TDNN systems featuring i-vector speaker adaptation suggest the proposed Bayesian adaptation approaches consistently outperform the adapted systems using deterministic parameters on the NIST Hub5'00 and RT03 evaluation sets in both unsupervised test time speaker adaptation and speaker adaptive training .
Revised:  Experiments conducted on 300-hour speed perturbed Switchboard corpus trained LF-MMI TDNN/CNN-TDNN systems featuring i-vector speaker adaptation suggest the proposed Bayesian adaptation approaches consistently outperform the adapted systems using deterministic parameters on the NIST Hub5'00 and RT03 evaluation sets in both unsupervised test time speaker adaptation and speaker adaptive training .
Edit intent: clarity
Original:  Experiments conducted on 300-hour speed perturbed Switchboard corpus trained LF-MMI factored TDNN/CNN-TDNN systems featuring i-vector speaker adaptation suggest the proposed Bayesian adaptation approaches consistently outperform the adapted systems using deterministic parameters on the NIST Hub5'00 and RT03 evaluation sets in both unsupervised test time speaker adaptation and speaker adaptive training .
Revised:  Experiments conducted on 300-hour speed perturbed Switchboard corpus trained LF-MMI factored TDNN/CNN-TDNN systems suggest the proposed Bayesian adaptation approaches consistently outperform the adapted systems using deterministic parameters on the NIST Hub5'00 and RT03 evaluation sets in both unsupervised test time speaker adaptation and speaker adaptive training .
Edit intent: clarity
Original:  Experiments conducted on 300-hour speed perturbed Switchboard corpus trained LF-MMI factored TDNN/CNN-TDNN systems featuring i-vector speaker adaptation suggest the proposed Bayesian adaptation approaches consistently outperform the adapted systems using deterministic parameters on the NIST Hub5'00 and RT03 evaluation sets in both unsupervised test time speaker adaptation and speaker adaptive training .
Revised:  Experiments conducted on 300-hour speed perturbed Switchboard corpus trained LF-MMI factored TDNN/CNN-TDNN systems featuring i-vector speaker adaptation suggest the proposed Bayesian adaptation approaches consistently outperform the deterministic adaptation on the NIST Hub5'00 and RT03 evaluation sets in both unsupervised test time speaker adaptation and speaker adaptive training .
Edit intent: clarity
Original:  The efficacy of the proposed Bayesian adaptation techniques is further demonstrated in a comparison against the state-of-the-art performance obtained on the same task using the most recent hybrid and end-to-end systems reported in the literature.
Revised:  The efficacy of the proposed Bayesian adaptation techniques is further demonstrated in a comparison against the state-of-the-art performance obtained on the same task using the most recent systems reported in the literature.
Edit intent: clarity
Original: In this work, we consider the complexity and throughput limits of the Fast Fourier Transform ( FFT) algorithm having in mind the unprecedented number of points (subcarriers) N expected in future waveforms.
Revised: This paper studies the impact of computational complexity on the throughput limits of the Fast Fourier Transform ( FFT) algorithm having in mind the unprecedented number of points (subcarriers) N expected in future waveforms.
Edit intent: clarity
Original:  Based on the spectro-computational analysis, we verify that the FFT complexity to process an N-subcarrier symbol grows faster than the number of bits in the  symbol.
Revised:  Based on the spectro-computational complexity (SC) analysis, it is verified that the complexity of an N-point FFT grows faster than the number of bits in the symbol.
Edit intent: clarity
Original:  Thus, the useful throughput of FFT nullifies as Ngrows . Also, because FFT demands N to be a power of two 2^i ( for some i>0), the spectrum widening causes the FFT complexity to grow exponentially on i, i.e. O(2^ii).
Revised:  Thus, the useful throughput of FFT nullifies on N . Also, because FFT demands N to be a power of two 2^i ( for some i>0), the spectrum widening causes the FFT complexity to grow exponentially on i, i.e. O(2^ii).
Edit intent: clarity
Original:  Thus, the useful throughput of FFT nullifies as Ngrows . Also, because FFT demands N to be a power of two 2^i ( for some i>0), the spectrum widening causes the FFT complexity to grow exponentially on i, i.e. O(2^ii).
Revised:  Thus, the useful throughput of FFT nullifies as Ngrows . Also, because FFT demands N to be a power of two 2^i ( i>0), the spectrum widening causes the FFT complexity to grow exponentially on i, i.e. O(2^ii).
Edit intent: clarity
Original:  Thus, the useful throughput of FFT nullifies as Ngrows . Also, because FFT demands N to be a power of two 2^i ( for some i>0), the spectrum widening causes the FFT complexity to grow exponentially on i, i.e. O(2^ii).
Revised:  Thus, the useful throughput of FFT nullifies as Ngrows . Also, because FFT demands N to be a power of two 2^i ( for some i>0), the spectrum widening leads to an exponential complexity on i, i.e. O(2^ii).
Edit intent: clarity
Original:  To overcome these limitations, we propose the Parameterized DFT (PDFT)algorithm, which builds on the parameterized complexity technique and the classic O(N^2) DFT algorithm to replace an N-point DFT into N/ n (n >0) smaller n-point DFTs.
Revised:  To overcome these limitations, we propose the Parameterized DFT (PDFT)algorithm, which builds on the parameterized complexity technique and the classic O(N^2) DFT algorithm to replace an N-point FFT is replaced by N/ n (n >0) smaller n-point DFTs.
Edit intent: clarity
Original:  To overcome these limitations, we propose the Parameterized DFT (PDFT)algorithm, which builds on the parameterized complexity technique and the classic O(N^2) DFT algorithm to replace an N-point DFT into N/ n (n >0) smaller n-point DFTs.
Revised:  To overcome these limitations, we propose the Parameterized DFT (PDFT)algorithm, which builds on the parameterized complexity technique and the classic O(N^2) DFT algorithm to replace an N-point DFT into N/ L (L >0) smaller n-point DFTs.
Edit intent: clarity
Original:  By setting n = \Theta(1), we get a O(N) algorithm whose resulting waveform matches OFDM in its vectorized form (i.e. , Vector OFDM) but with the N= 2^i constraint relaxed . Besides, we also show that PDFT becomes multiplierless for n=2, requiring only \Theta(N ) complex sums.
Revised:  By setting n = \Theta(1), we get a O(N) algorithm whose resulting waveform matches OFDM in its vectorized form (i.e. , N= 2^i constraint relaxed . Besides, we also show that PDFT becomes multiplierless for n=2, requiring only \Theta(N ) complex sums.
Edit intent: coherence
Original:  With the structure of advanced residual networks , sequential recommender models can be stacked with many hidden layers, e.g., up to 100 layers on real-world SR datasets.
Revised:  With advanced network architectures , sequential recommender models can be stacked with many hidden layers, e.g., up to 100 layers on real-world SR datasets.
Edit intent: clarity
Original:  With the structure of advanced residual networks , sequential recommender models can be stacked with many hidden layers, e.g., up to 100 layers on real-world SR datasets.
Revised:  With the structure of advanced residual networks , sequential recommender models can be stacked with many hidden layers, e.g., up to 100 layers on real-world recommendation datasets.
Edit intent: clarity
Original:  Training such a deep network requires expensive computation and longer training time, especially in situations when there are tens of billions of user-item interactions.
Revised:  Training such a deep network is difficult because it can be computationally very expensive and takes much longer time, especially in situations when there are tens of billions of user-item interactions.
Edit intent: clarity
Original:  Training such a deep network requires expensive computation and longer training time, especially in situations when there are tens of billions of user-item interactions.
Revised:  Training such a deep network requires expensive computation and longer training time, especially in situations where there are tens of billions of user-item interactions.
Edit intent: fluency
Original:  Specifically, we first offer an important insight that residual layers/blocks in a well-trained deep SR model have similar distribution . Enlightened by this, we propose progressively stacking such pre-trained residual layers/blocks  so as to yield a  deeper but easier-to-train SR model.
Revised:  Specifically, we first offer an important insight that hidden layers/blocks in a well-trained deep SR model have similar distribution . Enlightened by this, we propose progressively stacking such pre-trained residual layers/blocks so as to yield a deeper but easier-to-train SR model.
Edit intent: clarity
Original:  Specifically, we first offer an important insight that residual layers/blocks in a well-trained deep SR model have similar distribution . Enlightened by this, we propose progressively stacking such pre-trained residual layers/blocks  so as to yield a  deeper but easier-to-train SR model.
Revised:  Specifically, we first offer an important insight that residual layers/blocks in a well-trained deep SR model have very similar distributions . Enlightened by this, we propose progressively stacking such pre-trained residual layers/blocks so as to yield a deeper but easier-to-train SR model.
Edit intent: clarity
Original:  Specifically, we first offer an important insight that residual layers/blocks in a well-trained deep SR model have similar distribution . Enlightened by this, we propose progressively stacking such pre-trained residual layers/blocks  so as to yield a  deeper but easier-to-train SR model.
Revised:  Specifically, we first offer an important insight that residual layers/blocks in a well-trained deep SR model have similar distribution . Enlightened by this, we propose the stacking operation on the pre-trained residual layers/blocks so as to yield a deeper but easier-to-train SR model.
Edit intent: clarity
Original:  Specifically, we first offer an important insight that residual layers/blocks in a well-trained deep SR model have similar distribution . Enlightened by this, we propose progressively stacking such pre-trained residual layers/blocks  so as to yield a  deeper but easier-to-train SR model.
Revised:  Specifically, we first offer an important insight that residual layers/blocks in a well-trained deep SR model have similar distribution . Enlightened by this, we propose progressively stacking such pre-trained layers/blocks so as to yield a deeper but easier-to-train SR model.
Edit intent: fluency
Original:  Specifically, we first offer an important insight that residual layers/blocks in a well-trained deep SR model have similar distribution . Enlightened by this, we propose progressively stacking such pre-trained residual layers/blocks  so as to yield a  deeper but easier-to-train SR model.
Revised:  Specifically, we first offer an important insight that residual layers/blocks in a well-trained deep SR model have similar distribution . Enlightened by this, we propose progressively stacking such pre-trained residual layers/blocks so as to yield a much deeper but easier-to-train SR model.
Edit intent: clarity
Original:  We validate the proposed StackRec by instantiating with two state-of-the-art SR models in three practical scenarios and real-world datasets.
Revised:  We validate the performance of StackRec by instantiating with two state-of-the-art SR models in three practical scenarios and real-world datasets.
Edit intent: clarity
Original:  We validate the proposed StackRec by instantiating with two state-of-the-art SR models in three practical scenarios and real-world datasets.
Revised:  We validate the proposed StackRec by instantiating with two state-of-the-art SR models in three practical scenarios with real-world datasets.
Edit intent: fluency
Original:  Extensive experiments show that StackRec achieves not only comparable performance, but also significant acceleration in training time, compared to SR models that are trained from scratch. 
Revised:  Extensive experiments show that StackRec achieves not only comparable performance, but also substantial acceleration in training time, compared to SR models that are trained from scratch. 
Edit intent: clarity
Original:  We find that the current focus of the field does not fully align with participants' wishes.
Revised:  We find that the current direction of the field does not fully align with participants' wishes.
Edit intent: clarity
Original:  Third, usefulness is an important aspect of summarization that should be included in our evaluation methodology;
Revised:  Second,
Edit intent: coherence
Original: Query auto-completion  is a fundamental feature in search engines where the task is to suggest plausible completions of a prefix typed in the search bar.
Revised: Query auto-completion (QAC) is a fundamental feature in search engines where the task is to suggest plausible completions of a prefix typed in the search bar.
Edit intent: clarity
Original:  Such session-aware query auto-completions can be generated by  sequence-to-sequence  models;
Revised:  Such session-aware QACs can be generated by sequence-to-sequence models;
Edit intent: clarity
Original:  In this paper, we provide a solution to this problem: we take the novel approach of modeling session-aware query auto-completion as an eXtreme Multi-Label Ranking (XMR) problem where the input is the previous query in the session and the user's current prefix, while the output space is the set of  millions of queries entered by users in the recent past.
Revised:  In this paper, we provide a solution to this problem: we take the novel approach of modeling session-aware QAC as an eXtreme Multi-Label Ranking (XMR) problem where the input is the previous query in the session and the user's current prefix, while the output space is the set of millions of queries entered by users in the recent past.
Edit intent: clarity
Original:  In this paper, we provide a solution to this problem: we take the novel approach of modeling session-aware query auto-completion as an eXtreme Multi-Label Ranking (XMR) problem where the input is the previous query in the session and the user's current prefix, while the output space is the set of  millions of queries entered by users in the recent past.
Revised:  In this paper, we provide a solution to this problem: we take the novel approach of modeling session-aware query auto-completion as an eXtreme Multi-Label Ranking (XMR) problem where the input is the previous query in the session and the user's current prefix, while the output space is the set of tens of millions of queries entered by users in the recent past.
Edit intent: clarity
Original:  in particular, for short prefixes with up to 3 characters , we see a 32\% improvement over baselines that meet latency requirements .
Revised:  in particular, for short prefixes of up to 3 characters , we see a 32\% improvement over baselines that meet latency requirements .
Edit intent: fluency
Original:  Next, deep features are extracted using multiple types of deep models (pre-trained models, generative models, generic neural networks, etc  ).
Revised:  Next, deep features are extracted using multiple types of deep models (pre-trained models, generative models, generic neural networks, etc . ).
Edit intent: fluency
Original: Computers can solve a wide spectrum of problems, but they can't solve all the problems , some problems can never have a solution, some problems can be, but they require more space the size of the universe, or more time than the age of the universe on our computational models.
Revised: Computers are known to solve a wide spectrum of problems, but they can't solve all the problems , some problems can never have a solution, some problems can be, but they require more space the size of the universe, or more time than the age of the universe on our computational models.
Edit intent: clarity
Original:  It might not seem so but Complexity Theory tells us that if we had an algorithm that could solve Sudoku efficiently then we could adapt it to predict for protein folding.
Revised:  It might not seem so but complexity theory tells us that if we had an algorithm that could solve Sudoku efficiently then we could adapt it to predict for protein folding.
Edit intent: fluency
Original:  It might not seem so but Complexity Theory tells us that if we had an algorithm that could solve Sudoku efficiently then we could adapt it to predict for protein folding.
Revised:  It might not seem so but Complexity Theory tells us that if we had an algorithm that could solve sudoku efficiently then we could adapt it to predict for protein folding.
Edit intent: fluency
Original:   Here, we shall prove how the game "Celeste" also shares such property by proving it to be NP-complete and then later show how a small change in it makes the game presumably harder to compute - to be precise, PSPACE-complete.
Revised:  Here, we shall prove how the game "Celeste" is NP-complete and then later show how a small change in it makes the game presumably harder to compute - to be precise, PSPACE-complete.
Edit intent: clarity
Original:   Here, we shall prove how the game "Celeste" also shares such property by proving it to be NP-complete and then later show how a small change in it makes the game presumably harder to compute - to be precise, PSPACE-complete.
Revised:  Here, we shall prove how the game "Celeste" also shares such property by proving it to be NP-complete . Later, we also show how a small change in it makes the game presumably harder to compute - to be precise, PSPACE-complete.
Edit intent: coherence
Original:  In the end we also present a compilation of generalized meta-theorems originally formulated by Giovanni Viglietta  .
Revised:  In the end we also present a compilation of generalized meta-theorems originally formulated by Giovanni Viglietta are also presented .
Edit intent: coherence
Original:  Train capacity, which has often been overlooked in the literature, is an important input that exhibits a lot of variability .
Revised:  Train capacity, which has often been overlooked in the literature, is an important input that exhibits a lot of variabilities .
Edit intent: clarity
Original:  Recently, an interesting new approach based on deep learning has been introduced by E, Han, and Jentzen [1] , [2].
Revised:  Recently, an interesting new approach based on deep learning has been introduced by E, Han, and Jentzen [1] [2].
Edit intent: fluency
Original:  The network is able to approximate  the solutions of the Kolmogorov equation with polynomial complexity in whole spatial domains , therefore avoiding the curse of dimensionality .
Revised:  The network is able to approximate the solutions of the Kolmogorov equation with polynomial complexity in whole spatial domains .
Edit intent: coherence
Original:  We compare the performance of the associated networks, on benchmarked examples, and show that, for some discretization schemes, improvements in the accuracy are possible without affecting the  computational complexity.
Revised:  We compare the performance of the associated networks, on benchmarked examples, and show that, for some discretization schemes, improvements in the accuracy are possible without affecting the observed computational complexity.
Edit intent: clarity