wanyu/IteraTeR_full_sent huggingface dataset:
Original:  The canonical Smatch metric (Cai and Knight, 2013) aligns variables from one graph to another and compares the matching triples. The recently released SemBleu metric (Song and Gildea, 2019) is based on the machine-translation metric Bleu (Papineni et al., 2002) , increasing computational efficiency by ablating a variable-alignment step and aiming at capturing more global graph properties.
Revised:  The canonical Smatch metric (Cai and Knight, 2013) aligns the variables of two graphs and assesses triple matches. The recent SemBleu metric (Song and Gildea, 2019) is based on the machine-translation metric Bleu (Papineni et al., 2002) , increasing computational efficiency by ablating a variable-alignment step and aiming at capturing more global graph properties.
Edit intent: clarity
Original:  The recently released SemBleu metric (Song and Gildea, 2019) is based on the machine-translation metric Bleu (Papineni et al., 2002) , increasing computational efficiency by ablating a variable-alignment step and aiming at capturing more global graph properties.
Revised:  The recently released SemBleu metric (Song and Gildea, 2019) is based on the machine-translation metric Bleu (Papineni et al., 2002) and increases computational efficiency by ablating a variable-alignment step and aiming at capturing more global graph properties.
Edit intent: coherence
Original:  The recently released SemBleu metric (Song and Gildea, 2019) is based on the machine-translation metric Bleu (Papineni et al., 2002) , increasing computational efficiency by ablating a variable-alignment step and aiming at capturing more global graph properties.
Revised:  The recently released SemBleu metric (Song and Gildea, 2019) is based on the machine-translation metric Bleu (Papineni et al., 2002) , increasing computational efficiency by ablating the variable-alignment step and aiming at capturing more global graph properties.
Edit intent: fluency
Original:  The recently released SemBleu metric (Song and Gildea, 2019) is based on the machine-translation metric Bleu (Papineni et al., 2002) , increasing computational efficiency by ablating a variable-alignment step and aiming at capturing more global graph properties. Our aims are threefold: i) we establish criteria that allow us to perform a principled comparison between metrics of symbolic meaning representations like AMR;
Revised:  The recently released SemBleu metric (Song and Gildea, 2019) is based on the machine-translation metric Bleu (Papineni et al., 2002) , increasing computational efficiency by ablating a variable-alignment . In this paper, i) we establish criteria that allow us to perform a principled comparison between metrics of symbolic meaning representations like AMR;
Edit intent: coherence
Original:  In this work, we gather the best of both encoding strategies, proposing novel models that encode an input graph combining both global and local node contexts . Our approaches are able to learn better contextualized node embeddings for text generation .
Revised:  In this work, we gather the best of both encoding strategies, proposing novel models that encode an input graph combining both global and local node contexts , in order to learn better contextualized node embeddings for text generation .
Edit intent: coherence
Original:  In our experiments, we demonstrate that our models lead to significant improvements in KG-to-text generation, achieving BLEU scores of 17.81 on AGENDA dataset, and 63.10 on the WebNLG dataset for seen categories, outperforming the state of the art by 3.51 and 2.51 points, respectively.
Revised:  In our experiments, we demonstrate that our approaches lead to significant improvements in KG-to-text generation, achieving BLEU scores of 17.81 on AGENDA dataset, and 63.10 on the WebNLG dataset for seen categories, outperforming the state of the art by 3.51 and 2.51 points, respectively.
Edit intent: clarity
Original:  In our experiments, we demonstrate that our models lead to significant improvements in KG-to-text generation, achieving BLEU scores of 17.81 on AGENDA dataset, and 63.10 on the WebNLG dataset for seen categories, outperforming the state of the art by 3.51 and 2.51 points, respectively.
Revised:  In our experiments, we demonstrate that our models lead to significant improvements on two graph-to-text datasets achieving BLEU scores of 17.81 on AGENDA dataset, and 63.10 on the WebNLG dataset for seen categories, outperforming the state of the art by 3.51 and 2.51 points, respectively.
Edit intent: clarity
Original:  In our experiments, we demonstrate that our models lead to significant improvements in KG-to-text generation, achieving BLEU scores of 17.81 on AGENDA dataset, and 63.10 on the WebNLG dataset for seen categories, outperforming the state of the art by 3.51 and 2.51 points, respectively.
Revised:  In our experiments, we demonstrate that our models lead to significant improvements in KG-to-text generation, achieving BLEU scores of 18.01 on AGENDA dataset, and 63.10 on the WebNLG dataset for seen categories, outperforming the state of the art by 3.51 and 2.51 points, respectively.
Edit intent: meaning-changed
Original:  In our experiments, we demonstrate that our models lead to significant improvements in KG-to-text generation, achieving BLEU scores of 17.81 on AGENDA dataset, and 63.10 on the WebNLG dataset for seen categories, outperforming the state of the art by 3.51 and 2.51 points, respectively.
Revised:  In our experiments, we demonstrate that our models lead to significant improvements in KG-to-text generation, achieving BLEU scores of 17.81 on AGENDA dataset, and 63.69 on the WebNLG dataset for seen categories, outperforming the state of the art by 3.51 and 2.51 points, respectively.
Edit intent: meaning-changed
Original:  In our experiments, we demonstrate that our models lead to significant improvements in KG-to-text generation, achieving BLEU scores of 17.81 on AGENDA dataset, and 63.10 on the WebNLG dataset for seen categories, outperforming the state of the art by 3.51 and 2.51 points, respectively.
Revised:  In our experiments, we demonstrate that our models lead to significant improvements in KG-to-text generation, achieving BLEU scores of 17.81 on AGENDA dataset, and 63.10 on the WebNLG dataset for seen categories, outperforming state-of-the-art models by 3.7 and 3.1 points, respectively.
Edit intent: meaning-changed
Original:  We confirm some , but not all, prior results as to motif significance in these data sets. Code and datasets are freely available at URL
Revised:  We confirm some , but not in a yeast regulatory network. We also confirm using this method previous research showing that under-representation of the cyclic triangle (feedback loop) can be explained as a consequence of other topological features. Conclusion: ERGMs allow the over- or under-representation of multiple non-independent structural configurations to be tested simultaneously in a single model. This can overcome some of the problems recently identified in mainstream methods for motif identification, specifically assumptions of normally distributed motif frequencies and independence of motifs.
Edit intent: meaning-changed
Original:  However, there is disagreement on the statistical significance of these motifs and the methods used to determine it . Exponential random graph models (ERGMs) are a class of statistical model that can overcome some of the shortcomings of commonly used methods for testing the statistical significance of motifs.
Revised:  However, there is disagreement as to the statistical significance of these motifs and the methods used to determine it . Exponential random graph models (ERGMs) are a class of statistical model that can overcome some of the shortcomings of commonly used methods for testing the statistical significance of motifs.
Edit intent: clarity
Original:  However, there is disagreement on the statistical significance of these motifs and the methods used to determine it . Exponential random graph models (ERGMs) are a class of statistical model that can overcome some of the shortcomings of commonly used methods for testing the statistical significance of motifs.
Revised:  However, there is disagreement on the statistical significance of these motifs , and there are potential problems with standard methods for estimating this significance . Exponential random graph models (ERGMs) are a class of statistical model that can overcome some of the shortcomings of commonly used methods for testing the statistical significance of motifs.
Edit intent: meaning-changed
Original:  We confirm the over-representation of triangles in the PPI network and transitive triangles (feed-forward loop) in an E. coli regulatory network, but not in a yeast regulatory network.
Revised:  ERGM models indicate over-representation of triangles in the PPI network and transitive triangles (feed-forward loop) in an E. coli regulatory network, but not in a yeast regulatory network.
Edit intent: meaning-changed
Original:  We confirm the over-representation of triangles in the PPI network and transitive triangles (feed-forward loop) in an E. coli regulatory network, but not in a yeast regulatory network.
Revised:  We confirm the over-representation of triangles in the PPI network , and confirm results from previous research as to over-representation of transitive triangles (feed-forward loop) in an E. coli regulatory network, but not in a yeast regulatory network.
Edit intent: meaning-changed
Original:  We also confirm using this method previous research showing that under-representation of the cyclic triangle (feedback loop) can be explained as a consequence of other topological features . Conclusion: ERGMs allow the over- or under-representation of multiple non-independent structural configurations to be tested simultaneously in a single model.
Revised:  We also confirm , using ERGMs, previous research showing that under-representation of the cyclic triangle (feedback loop) can be explained as a consequence of other topological features . Conclusion: ERGMs allow the over- or under-representation of multiple non-independent structural configurations to be tested simultaneously in a single model.
Edit intent: clarity
Original:  We also confirm using this method previous research showing that under-representation of the cyclic triangle (feedback loop) can be explained as a consequence of other topological features . Conclusion: ERGMs allow the over- or under-representation of multiple non-independent structural configurations to be tested simultaneously in a single model. This can overcome some of the problems recently identified in mainstream methods for motif identification, specifically assumptions of normally distributed motif frequencies and independence of motifs .
Revised:  We also confirm using this method previous research showing that under-representation of the cyclic triangle (feedback loop) can be explained as a consequence of other topological features .
Edit intent: coherence
Original: Hawkes processes have been successfully applied to understand online information diffusion and popularity of online items. Most prior work concentrate on individually modeling successful diffusion cascades, while discarding smaller cascades which, however, account for a majority proportion of the available data. In this work , we propose a set of tools to leverage information in the small cascades: a joint fitting procedure that accounts for cascade size bias in the sample, a Borel mixture model and a clustering algorithm to uncover latent groups within these cascades , and the posterior final size distribution of Hawkes processes.
Revised: It is well-known that online behavior is long-tailed, with most cascaded actions being short and a few being very long. A prominent drawback in generative models for online events is the inability to describe unpopular items well. This work addresses these shortcomings by proposing dual mixture self-exciting processes to jointly learn from groups of cascades. We first start from the observation that maximum likelihood estimates for content virality and influence decay are separable in a Hawkes process. Next, our proposed model, which leverages a Borel mixture model and a clustering algorithm to uncover latent groups within these cascades , and the posterior final size distribution of Hawkes processes.
Edit intent: meaning-changed
Original:  In this work , we propose a set of tools to leverage information in the small cascades: a joint fitting procedure that accounts for cascade size bias in the sample, a Borel mixture model and a clustering algorithm to uncover latent groups within these cascades , and the posterior final size distribution of Hawkes processes. On a dataset of Twitter cascades, we show that , compared to the state-of-art models, the proposed method improves the generalization performance on unseen data, delivers better prediction for final popularity and provides means to characterize online content from the way Twitter users discuss about it .
Revised:  In this work , we propose a set of tools to leverage information in the small cascades: a joint fitting procedure that accounts for cascade size bias in the sample, a Borel mixture model and a kernel mixture model, jointly models the unfolding of a heterogeneous set of cascades. When applied to cascades of the same online items, the model directly characterizes their spread dynamics and supplies interpretable quantities, such as content virality and content influence decay, as well as methods for predicting the final content popularities. On two retweet cascade datasets -- one relating to YouTube videos and the second relating to controversial news articles -- we show that , compared to the state-of-art models, the proposed method improves the generalization performance on unseen data, delivers better prediction for final popularity and provides means to characterize online content from the way Twitter users discuss about it .
Edit intent: meaning-changed
Original:  On a dataset of Twitter cascades, we show that , compared to the state-of-art models, the proposed method improves the generalization performance on unseen data, delivers better prediction for final popularity and provides means to characterize online content from the way Twitter users discuss about it .
Revised:  On a dataset of Twitter cascades, we show that our models capture the differences between online items at the granularity of items, publishers and categories. In particular, we are able to distinguish between far-right, conspiracy, controversial and reputable online news articles based on how they diffuse through social media, achieving an F1 score of 0.945. On holdout datasets, we show that the dual mixture model provides, for reshare diffusion cascades especially unpopular ones, better generalization performance and, for online items, accurate item popularity predictions .
Edit intent: meaning-changed
Original:  In this work , we present a large-scale study focused on the correlations between language similarity and task performance, covering thousands of language pairs and four different tasks: BLI, machine translation, parsing, and POS tagging .
Revised:  In this work we present a large-scale study focused on the correlations between language similarity and task performance, covering thousands of language pairs and four different tasks: BLI, machine translation, parsing, and POS tagging .
Edit intent: fluency
Original:  In this work , we present a large-scale study focused on the correlations between language similarity and task performance, covering thousands of language pairs and four different tasks: BLI, machine translation, parsing, and POS tagging .
Revised:  In this work , we present a large-scale study focused on the correlations between monolingual embedding space similarity and task performance, covering thousands of language pairs and four different tasks: BLI, machine translation, parsing, and POS tagging .
Edit intent: meaning-changed
Original:  In this work , we present a large-scale study focused on the correlations between language similarity and task performance, covering thousands of language pairs and four different tasks: BLI, machine translation, parsing, and POS tagging .
Revised:  In this work , we present a large-scale study focused on the correlations between language similarity and task performance, covering thousands of language pairs and four different tasks: BLI, parsing, POS tagging and MT. We hypothesize that statistics of the spectrum of each monolingual embedding space indicate how well they can be aligned. We then introduce several isomorphism measures between two embedding spaces, based on the relevant statistics of their individual spectra .
Edit intent: meaning-changed
Original:  We propose a novel language distance measure, Eigenvalue Divergence (EVD), which quantifies the degree of isomorphism between two monolingual spaces. We empirically show that 1) language similarity scores derived from embedding-based EVD distances are strongly associated with performance observed in different cross-lingual tasks,  2) EVD outperforms other standard embedding-based language distance measuresacross the board, at the same time being computationally more tractable and easier to interpret.
Revised:  We empirically show that 1) language similarity scores derived from embedding-based EVD distances are strongly associated with performance observed in different cross-lingual tasks, 2) EVD outperforms other standard embedding-based language distance measuresacross the board, at the same time being computationally more tractable and easier to interpret.
Edit intent: clarity
Original:  We empirically show that 1) language similarity scores derived from embedding-based EVD distances are strongly associated with performance observed in different cross-lingual tasks,  2) EVD outperforms other standard embedding-based language distance measuresacross the board, at the same time being computationally more tractable and easier to interpret.
Revised:  We empirically show that 1) language similarity scores derived from such spectral isomorphism measures are strongly associated with performance observed in different cross-lingual tasks, 2) EVD outperforms other standard embedding-based language distance measuresacross the board, at the same time being computationally more tractable and easier to interpret.
Edit intent: meaning-changed
Original:  We empirically show that 1) language similarity scores derived from embedding-based EVD distances are strongly associated with performance observed in different cross-lingual tasks,  2) EVD outperforms other standard embedding-based language distance measuresacross the board, at the same time being computationally more tractable and easier to interpret.
Revised:  We empirically show that 1) language similarity scores derived from embedding-based EVD distances are strongly associated with performance observed in different cross-lingual tasks, and 2) EVD outperforms other standard embedding-based language distance measuresacross the board, at the same time being computationally more tractable and easier to interpret.
Edit intent: coherence
Original:  We empirically show that 1) language similarity scores derived from embedding-based EVD distances are strongly associated with performance observed in different cross-lingual tasks,  2) EVD outperforms other standard embedding-based language distance measuresacross the board, at the same time being computationally more tractable and easier to interpret.
Revised:  We empirically show that 1) language similarity scores derived from embedding-based EVD distances are strongly associated with performance observed in different cross-lingual tasks, 2) our spectral-based measures consistently outperform previous standard isomorphism measures, while being computationally more tractable and easier to interpret.
Edit intent: meaning-changed
Original:  Finally, we demonstrate that EVD captures information which is complementary to typologically driven language distance measures . We report that their combination yields even higher correlations with performance levels in all cross-lingual tasks .
Revised:  Finally, we demonstrate that EVD captures information which is complementary to typologically driven language distance measures , and the combination of measures from the two families yields even higher correlations with performance levels in all cross-lingual tasks .
Edit intent: meaning-changed
Original:  We hypothesize that cross-lingual models that fit into the source language word order might fail to handle target languages whose word orders are different. To test our conjecture, we build an order-agnostic model for cross-lingual sequence labeling tasks. Our model does not encode the word order information of the input sequences, and the predictions for each token are based on the attention on the whole sequence . Experimental results on dialogue natural language understanding, part-of-speech tagging, and named entity recognition tasks show that getting rid of word order information is able to achieve better zero-shot cross-lingual performance than baseline models .
Revised:  We hypothesize that cross-lingual models that fit into the source language word order might fail to handle target languages . To verify this hypothesis, we investigate whether making models insensitive to the word order information of the input sequences, and the predictions for each token are based on the attention on the whole sequence . Experimental results on dialogue natural language understanding, part-of-speech tagging, and named entity recognition tasks show that getting rid of word order information is able to achieve better zero-shot cross-lingual performance than baseline models .
Edit intent: clarity
Original:  Our model does not encode the word order information of the input sequences, and the predictions for each token are based on the attention on the whole sequence . Experimental results on dialogue natural language understanding, part-of-speech tagging, and named entity recognition tasks show that getting rid of word order information is able to achieve better zero-shot cross-lingual performance than baseline models .
Revised:  Our model does not encode the word order of the predictions for each token are based on the attention on the whole sequence . Experimental results on dialogue natural language understanding, part-of-speech tagging, and named entity recognition tasks show that getting rid of word order information is able to achieve better zero-shot cross-lingual performance than baseline models .
Edit intent: coherence
Original:  Our model does not encode the word order information of the input sequences, and the predictions for each token are based on the attention on the whole sequence . Experimental results on dialogue natural language understanding, part-of-speech tagging, and named entity recognition tasks show that getting rid of word order information is able to achieve better zero-shot cross-lingual performance than baseline models .
Revised:  Our model does not encode the word order information of the input sequences, and the source language can improve the adaptation performance in target languages. To do so, we reduce the source language word order information fitted to sequence encoders and observe the performance changes. In addition, based on this hypothesis, we propose a new method for fine-tuning multilingual BERT in downstream cross-lingual sequence labeling tasks . Experimental results on dialogue natural language understanding, part-of-speech tagging, and named entity recognition tasks show that getting rid of word order information is able to achieve better zero-shot cross-lingual performance than baseline models .
Edit intent: meaning-changed