wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Furthermore, we investigate and propose solutions to two previously overlooked  scenarios of data scarcity in SLU: i) Rich-in-Ontology: ontology information with numerous valid dialogue acts are given;
Revised:  Furthermore, we investigate and propose solutions to two previously overlooked semi-supervised learning scenarios of data scarcity in SLU: i) Rich-in-Ontology: ontology information with numerous valid dialogue acts are given;
Edit intent: meaning-changed
Original:  Furthermore, we investigate and propose solutions to two previously overlooked  scenarios of data scarcity in SLU: i) Rich-in-Ontology: ontology information with numerous valid dialogue acts are given;
Revised:  Furthermore, we investigate and propose solutions to two previously overlooked scenarios of data scarcity in SLU: i) Rich-in-Ontology: ontology information with numerous valid dialogue acts is given;
Edit intent: fluency
Original:  However, reference summaries tend to be position-biased and constructed in an under-constrained fashion, especially for benchmark datasets in the news domain . We propose a neural framework that can flexibly control which sub-aspect functions (i.e. importance, diversity, position)  to focus on during summary generation.
Revised:  However, reference summaries tend to be position-biased and constructed in an under-constrained fashion, especially for benchmark datasets in the news domain , neural models easily overfit by leveraging position-related features due to the prevalence of the inverted pyramid writing style. In addition, there is an unmet need to generate a variety of summaries for different users. In this paper, we propose a neural framework that can flexibly control which sub-aspect functions (i.e. importance, diversity, position) to focus on during summary generation.
Edit intent: meaning-changed
Original:  We propose a neural framework that can flexibly control which sub-aspect functions (i.e. importance, diversity, position)  to focus on during summary generation.
Revised:  We propose a neural framework that can flexibly control which sub-aspect functions (i.e. importance, diversity, position) . These sub-aspect functions are regulated by a set of control codes to decide which sub-aspect to focus on during summary generation.
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Original:  These results suggest that a more flexible neural summarization framework can provide more control options to tailor to different application needs. This framework is useful because it is often difficult to know or articulate a priori what the user-preferences of certain applications are .
Revised:  These results suggest that a more flexible neural summarization framework can provide more control options could be desirable in tailoring to different user preferences, which is useful since it is often difficult to know or articulate a priori what the user-preferences of certain applications are .
Edit intent: clarity
Original:  To fill this important gap, we construct AdvRACE (Adversarial RACE), a new model-agnostic benchmark for evaluating the robustness of MRC models under six different types of test-time perturbations , including our novel superimposed attack and distractor construction attack .
Revised:  To fill this important gap, we construct AdvRACE (Adversarial RACE), a new model-agnostic benchmark for evaluating the robustness of MRC models under four different types of test-time perturbations , including our novel superimposed attack and distractor construction attack .
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Original:  To fill this important gap, we construct AdvRACE (Adversarial RACE), a new model-agnostic benchmark for evaluating the robustness of MRC models under six different types of test-time perturbations , including our novel superimposed attack and distractor construction attack .
Revised:  To fill this important gap, we construct AdvRACE (Adversarial RACE), a new model-agnostic benchmark for evaluating the robustness of MRC models under six different types of adversarial attacks , including our novel superimposed attack and distractor construction attack .
Edit intent: clarity
Original:  To fill this important gap, we construct AdvRACE (Adversarial RACE), a new model-agnostic benchmark for evaluating the robustness of MRC models under six different types of test-time perturbations , including our novel superimposed attack and distractor construction attack .
Revised:  To fill this important gap, we construct AdvRACE (Adversarial RACE), a new model-agnostic benchmark for evaluating the robustness of MRC models under six different types of test-time perturbations , including our novel distractor extraction and generation attacks .
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Original:  We show that current state-of-the-art (SOTA) models are vulnerable to these simple black-box attacks. Our benchmark is constructed automatically based on the existing RACE benchmark, and thus the construction pipeline can be easily adopted by other tasks and datasets. We will release the data and source codes to facilitate future work. We hope that our work will encourage more research on improving the robustness of MRC and other NLU models .
Revised:  We show that current state-of-the-art (SOTA) models are vulnerable to all of these attacks. We conclude that there is substantial room for building more robust MRC models and our benchmark can help motivate and measure progress in this area. We release our data and code at URL .
Edit intent: clarity
Original:  In this work , we propose a scoring function that is specifically designed to identify low-quality utterance--response pairs to filter noisy training data. Our scoring function models the naturalness of the interconnection within dialogue pairs and their content-relatedness, which is based on previous findings in dialogue response generation and linguistics .
Revised:  In this work , we propose a method for scoring the quality of utterance pairs in terms of their connectivity and relatedness. The proposed scoring method is designed based on findings widely shared in the dialogue and linguistics research communities. We demonstrate that it has a relatively good correlation with the human judgment of dialogue response generation and linguistics .
Edit intent: coherence
Original:  MVAN effectively captures the question-relevant information from the dialog history with two different textual-views (i.e., Topic Aggregation and Context Matching), and integrates multimodal representations with two-step fusion process.
Revised:  MVAN effectively captures the question-relevant information from the dialog history with two complementary modules (i.e., Topic Aggregation and Context Matching), and integrates multimodal representations with two-step fusion process.
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Original:  MVAN effectively captures the question-relevant information from the dialog history with two different textual-views (i.e., Topic Aggregation and Context Matching), and integrates multimodal representations with two-step fusion process.
Revised:  MVAN effectively captures the question-relevant information from the dialog history with two different textual-views (i.e., Topic Aggregation and Context Matching), and builds multimodal representations through sequential alignment processes (i.e., Modality Alignment).
Edit intent: clarity
Original:  Experimental results on VisDial v1.0 and v0.9 benchmarks show the effectiveness of our proposed model, which outperforms the previous state-of-the-art methods with respect to all evaluation metrics.
Revised:  Experimental results on VisDial v1.0 dataset show the effectiveness of our proposed model, which outperforms the previous state-of-the-art methods with respect to all evaluation metrics.
Edit intent: clarity
Original:  While the classical Kelly betting problem can be solved via standard concave programming technique, an alternative but attractive approach is to invoke a Taylor-based approximation, which recast the problem into quadratic programming and obtain the closed-form approximate solution.
Revised:  While the classical Kelly betting problem can be solved via standard concave programming technique, an alternative but attractive approach is to invoke a Taylor-based approximation, which recasts the problem into quadratic programming and obtain the closed-form approximate solution.
Edit intent: fluency
Original:  Specifically, the best achievable betting performance, positivity of expected cumulative gain or loss and it associated variance, expected growth property, variance of logarithmic growth, and results related to the so-called survivability (no bankruptcy) are provided.
Revised:  Specifically, the best achievable betting performance, positivity of expected cumulative gain or loss and its associated variance, expected growth property, variance of logarithmic growth, and results related to the so-called survivability (no bankruptcy) are provided.
Edit intent: fluency
Original: Exposing diverse subword segmentations to neural machine translation (NMT) models often improves the robustness of machine translation . As NMT models experience various subword candidates , they become more robust to segmentation errors .
Revised: Exposing diverse subword segmentations to neural machine translation (NMT) models often improves the robustness of machine translation as NMT models can experience various subword candidates , they become more robust to segmentation errors .
Edit intent: clarity
Original:  We experimentally show that our model-based adversarial samples effectively encourage NMT models to be less sensitive to segmentation errors and improve the robustness of NMT models in low-resource  datasets.
Revised:  We experimentally show that our model-based adversarial samples effectively encourage NMT models to be less sensitive to segmentation errors and improve the robustness of NMT models in low-resource and out-domain datasets.
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Original:  In this paper, we place a challenging restriction to conventional few-shot relation classification by additionally limiting the amount of training data . We also propose a few-shot learning framework for relation classification, which is particularly powerful when the training data is very small.
Revised:  In this paper, we place a challenging restriction to conventional few-shot relation classification by additionally limiting the amount of data available at training time. We propose a few-shot learning framework for relation classification, which is particularly powerful when the training data is very small.
Edit intent: clarity
Original:  Experimental results demonstrate that our framework brings performance gains for most underlying classification models, and outperforms the state-of-the-art results given little training data(e.g., on TinyRel-CM dataset and FewRel-dataset Han et al., 2018%DIFDELCMD < ] %%% with reduced training set), and achieves competitive results with sufficiently large training data (e.g., on FewRel dataset with full training data) .
Revised:  Experimental results demonstrate that our framework brings performance gains for most underlying classification models, outperforms the state-of-the-art results given little training data(e.g., on TinyRel-CM dataset and FewRel-dataset Han et al., 2018%DIFDELCMD < ] %%% with reduced training set), and achieves competitive results with sufficiently large training data (e.g., on FewRel dataset with full training data) .
Edit intent: fluency
Original:  Experimental results demonstrate that our framework brings performance gains for most underlying classification models, and outperforms the state-of-the-art results given little training data(e.g., on TinyRel-CM dataset and FewRel-dataset Han et al., 2018%DIFDELCMD < ] %%% with reduced training set), and achieves competitive results with sufficiently large training data (e.g., on FewRel dataset with full training data) .
Revised:  Experimental results demonstrate that our framework brings performance gains for most underlying classification models, and outperforms the state-of-the-art results given Han et al., 2018%DIFDELCMD < ] %%% with reduced training set), and achieves competitive results with sufficiently large training data (e.g., on FewRel dataset with full training data) .
Edit intent: clarity
Original:  Experimental results demonstrate that our framework brings performance gains for most underlying classification models, and outperforms the state-of-the-art results given little training data(e.g., on TinyRel-CM dataset and FewRel-dataset Han et al., 2018%DIFDELCMD < ] %%% with reduced training set), and achieves competitive results with sufficiently large training data (e.g., on FewRel dataset with full training data) .
Revised:  Experimental results demonstrate that our framework brings performance gains for most underlying classification models, and outperforms the state-of-the-art results given little training data(e.g., on TinyRel-CM dataset and FewRel-dataset %DIFDELCMD < ] %%% with reduced training set), and achieves competitive results with sufficiently large training data (e.g., on FewRel dataset with full training data) .
Edit intent: coherence
Original:  Experimental results demonstrate that our framework brings performance gains for most underlying classification models, and outperforms the state-of-the-art results given little training data(e.g., on TinyRel-CM dataset and FewRel-dataset Han et al., 2018%DIFDELCMD < ] %%% with reduced training set), and achieves competitive results with sufficiently large training data (e.g., on FewRel dataset with full training data) .
Revised:  Experimental results demonstrate that our framework brings performance gains for most underlying classification models, and outperforms the state-of-the-art results given little training data(e.g., on TinyRel-CM dataset and FewRel-dataset Han et al., 2018%DIFDELCMD < ] %%% small training data, and achieves competitive results with sufficiently large training data (e.g., on FewRel dataset with full training data) .
Edit intent: clarity
Original:  Experimental results demonstrate that our framework brings performance gains for most underlying classification models, and outperforms the state-of-the-art results given little training data(e.g., on TinyRel-CM dataset and FewRel-dataset Han et al., 2018%DIFDELCMD < ] %%% with reduced training set), and achieves competitive results with sufficiently large training data (e.g., on FewRel dataset with full training data) .
Revised:  Experimental results demonstrate that our framework brings performance gains for most underlying classification models, and outperforms the state-of-the-art results given little training data(e.g., on TinyRel-CM dataset and FewRel-dataset Han et al., 2018%DIFDELCMD < ] %%% with reduced training set), and achieves competitive results with sufficiently large training data .
Edit intent: clarity
Original:  Recent advances introduced computational models to combine these multimodal sources and yielded strong performance on human-centric tasks . Nevertheless, most of the models are often black-box, which comes with the price of lacking interpretability.
Revised:  Recent advances introduced computational models to combine these multimodal sources and yielded strong performance on human-centric tasks such as sentiment analysis and emotion recognition are often black-box, which comes with the price of lacking interpretability.
Edit intent: meaning-changed
Original:  In this paper , we propose Multimodal Routing to separate the contributions to the prediction from each modality and the interactions between modalities. At the heart of our method is a routing mechanism that represents each prediction as a concept, i. e., a vector in a Euclidean space. The concept assumes a linear aggregation from the contributions of multimodal features. Then, the routing procedure iteratively 1) associates a feature and a concept by checking how this concept agrees with this feature and 2) updates the concept based on the associations.In our experiments, we provide both global and local interpretation using Multimodal Routing on sentiment analysis and emotion prediction, without loss of performance compared to state-of-the-art methods .
Revised:  In this paper , we propose Multimodal Routing , which dynamically adjusts weights between input modalities and output representations differently for each input sample. Multimodal routing can identify relative importance of both individual modalities and cross-modality features. Moreover, the weight assignment by routing allows us to interpret modality-prediction relationships not only globally (i.e. general trends over the whole dataset), but also locally for each single input sample, meanwhile keeping competitive performance compared to state-of-the-art methods .
Edit intent: clarity
Original: In our experiments, we provide both global and local interpretation using Multimodal Routing on sentiment analysis and emotion prediction, without loss of performance compared to state-of-the-art methods . For example, we observe that our model relies mostly on the text modality for neutral sentiment predictions, the acoustic modality for extremely negative predictions, and the text-acoustic bimodal interaction for extremely positive predictions .
Revised: In our experiments, we provide both global and local interpretation using Multimodal Routing on sentiment analysis and emotion prediction, without loss of performance compared to state-of-the-art methods .
Edit intent: clarity
Original:  The former refers to the literal definition of each propaganda technique, which is utilized to get class representations for regularizing the model parameters. The latter refers to logical consistency between coarse- and fine- grained predictions, which is used to regularize the training process with propositional Boolean expressions.
Revised:  The former refers to the logical consistency between coarse- and fine- grained predictions, which is used to regularize the training process with propositional Boolean expressions.
Edit intent: coherence
Original:  To alleviate this issue , we leverage the idea of continual learning to preserve the original cross-lingual ability of the pre-trained model when we fine-tune it to downstream cross-lingual tasks.
Revised:  To alleviate this problem , we leverage the idea of continual learning to preserve the original cross-lingual ability of the pre-trained model when we fine-tune it to downstream cross-lingual tasks.
Edit intent: clarity
Original:  The experiment on the cross-lingual sentence retrieval task shows that our fine-tuning approach can better preserve the cross-lingual ability of the pre-trained model . In addition, our method achieves better performance than other fine-tuning baselines on  zero-shot cross-lingual part-of-speech tagging and named entity recognition tasks.
Revised:  The experiment on the cross-lingual sentence retrieval task shows that our fine-tuning approach can better preserve the cross-lingual ability of the pre-trained model in a sentence retrieval task. Our methods also achieve better performance than other fine-tuning baselines on zero-shot cross-lingual part-of-speech tagging and named entity recognition tasks.
Edit intent: clarity
Original:  We show that by initially training a subword model based on this segmentation and then finetuning it on characters, we can obtain a neural machine translation model that works at the character level without requiring segmentation. Without changing the vanilla 6-layer Transformer Base architecture , we train purely character-level models .
Revised:  We show that by initially training a subword model based on this segmentation and then finetuning it on characters, we can obtain a neural machine translation model that works at the character level without requiring token segmentation. We use only the vanilla 6-layer Transformer Base architecture , we train purely character-level models .
Edit intent: coherence
Original: Within the context of stochastic probing with commitment, we consider the stochastic rewards problem, that is, the one sided online bipartite matching problem where edges adjacent to an online node must be probed to determine if they exist, based on known edge probabilities.
Revised: Within the context of stochastic probing with commitment, we consider the online stochastic matching problem; that is, the one sided online bipartite matching problem where edges adjacent to an online node must be probed to determine if they exist, based on known edge probabilities.
Edit intent: clarity
Original:  We consider the competitiveness of online algorithms in four different models. Namely, we consider the following stochastic reward problems: when the stochastic graph is not known and the order of online arrivals is determined uniformly at random (i.e., the ROM model) ;
Revised:  We study this problem in the generality of a patience (or budget) constraint which limits the number of probes that can be made to edges adjacent to an online node. The patience constraint results in modelling and computational efficiency issues that are not encountered in the special cases of unit patience and full (i.e., the ROM model) ;
Edit intent: meaning-changed
Original:  Namely, we consider the following stochastic reward problems: when the stochastic graph is not known and the order of online arrivals is determined uniformly at random (i.e., the ROM model) ; when the stochastic graph is known to the algorithm and the order of arrival of online vertices is determined adversarially; when the stochastic graph is known and the order of arrival of online vertices is determined uniformly at random ; and finally when the online vertices are generated i.i.d. from a known distribution on the vertices of a known stochastic (type)graph .
Revised:  Namely, we consider the following stochastic reward problems: when the stochastic graph is not known and the order of online arrivals is determined uniformly at random (i.e., unlimited) patience. The stochastic matching problem leads to a variety of settings. Our main contribution is to provide a new LP relaxation and a unified approach for establishing new and improved competitive bounds in three different input model settings (namely, adversarial, random order, and known i.i.d. from a known distribution on the vertices of a known stochastic (type)graph .
Edit intent: meaning-changed
Original:  We study these models under various assumptions on the patience (number of given probes)of the online vertices, and whether edges or offline vertices are weighted in determining the stochastic reward.
Revised:  We establish competitive bounds in these settings, all of which generalize the standard non-stochastic setting when edges do not need to be probed (i.e., exist with certainty). All of our competitive ratios hold for arbitrary edge probabilities and patience constraints: (1) A 1-1/e ratio when the stochastic graph is known, offline vertices are weighted in determining the stochastic reward.
Edit intent: meaning-changed
Original:  We study these models under various assumptions on the patience (number of given probes)of the online vertices, and whether edges or offline vertices are weighted in determining the stochastic reward. Our main contribution is to establish new and improved competitive bounds in these four models.In all settings, there is one ideal benchmark (the optimal offline probing algorithm)relative to which the competitive ratio is defined.For establishing competitive ratios we introduce a new LP relaxation which upper bounds the performance of the ideal benchmark .
Revised:  We study these models under various assumptions on the patience (number of given probes)of the online vertices, and whether edges or offline vertices are weighted and online arrivals are adversarial. (2) A 1-1/e ratio when the stochastic graph is known, edges are weighted, and online arrivals are given in random order (i.e., in ROM, the random order model). (3) A 1-1/e ratio when online arrivals are drawn i.i.d. from a known stochastic type graph and edges are weighted. (4) A (tight) 1/e ratio when the stochastic graph is unknown, edges are weighted and online arrivals are given in random order .
Edit intent: meaning-changed