wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We find that a BERT model is strikingly successful at the systematic generalization task we pose using this dataset , and our active manipulations of model-internal vectors help us understand why: despite the densely interconnected nature of the BERT architecture, the learned model embeds modular, general theories of lexical entailment relations .
Revised:  In our behavioral evaluations, we find that models trained on general-purpose NLI datasets fail systematically on MoNLI examples containing negation, but that MoNLI fine-tuning addresses this failure. In our structural evaluations, we look for evidence that our top-performing BERT-based model has learned to implement the monotonicity algorithm behind MoNLI. Probes yield evidence consistent with this conclusion , and our active manipulations of model-internal vectors help us understand why: despite the densely interconnected nature of the BERT architecture, the learned model embeds modular, general theories of lexical entailment relations .
Edit intent: meaning-changed
Original:  We find that a BERT model is strikingly successful at the systematic generalization task we pose using this dataset , and our active manipulations of model-internal vectors help us understand why: despite the densely interconnected nature of the BERT architecture, the learned model embeds modular, general theories of lexical entailment relations .
Revised:  We find that a BERT model is strikingly successful at the systematic generalization task we pose using this dataset , and our intervention experiments bolster this, showing that the causal dynamics of the BERT architecture, the learned model embeds modular, general theories of lexical entailment relations .
Edit intent: clarity
Original:  We find that a BERT model is strikingly successful at the systematic generalization task we pose using this dataset , and our active manipulations of model-internal vectors help us understand why: despite the densely interconnected nature of the BERT architecture, the learned model embeds modular, general theories of lexical entailment relations .
Revised:  We find that a BERT model is strikingly successful at the systematic generalization task we pose using this dataset , and our active manipulations of model-internal vectors help us understand why: despite the densely interconnected nature of the model mirror the causal dynamics of this algorithm on subsets of MoNLI. This suggests that the BERT model at least partially embeds a theory of lexical entailment relations .
Edit intent: meaning-changed
Original:  We find that a BERT model is strikingly successful at the systematic generalization task we pose using this dataset , and our active manipulations of model-internal vectors help us understand why: despite the densely interconnected nature of the BERT architecture, the learned model embeds modular, general theories of lexical entailment relations .
Revised:  We find that a BERT model is strikingly successful at the systematic generalization task we pose using this dataset , and our active manipulations of model-internal vectors help us understand why: despite the densely interconnected nature of the BERT architecture, the learned model embeds modular, general theories of lexical entailment and negation at an algorithmic level .
Edit intent: meaning-changed
Original:  In this paper, we propose to benchmark coherence models on a number of synthetic and downstream tasks. In particular, we evaluate well-known traditional and neural coherence models on  sentence ordering tasks, and also on three downstream applications including coherence evaluation for machine translation, summarization and next utterance prediction .
Revised:  However, coherence models are typically evaluated only on synthetic tasks, which may not be representative of their performance in downstream applications. To investigate how representative the synthetic tasks are of downstream use cases, we conduct experiments on benchmarking well-known traditional and neural coherence models on sentence ordering tasks, and also on three downstream applications including coherence evaluation for machine translation, summarization and next utterance prediction .
Edit intent: clarity
Original:  In particular, we evaluate well-known traditional and neural coherence models on  sentence ordering tasks, and also on three downstream applications including coherence evaluation for machine translation, summarization and next utterance prediction . We also show model produced rankings for pre-trained language model outputs as another use-case.
Revised:  In particular, we evaluate well-known traditional and neural coherence models on sentence ordering tasks, and also on three downstream applications including coherence evaluation for machine translation, summarization and next utterance prediction in retrieval-based dialog.
Edit intent: meaning-changed
Original:  For instance, our results imply that identifying a defective pair in a set of m points in the plane always requires \Omega(m^{ 4 / 7 }) rectangle-tests , and there exist configurations of m points for which O(m^{2/3}) rectangle-tests are sufficient, whereas to identify a single defective point in the plane, \Theta(m^{1/2}) tests are always necessary and sometimes sufficient.
Revised:  For instance, our results imply that identifying a defective pair in a set of m points in the plane always requires \Omega(m^{ 3 / 7 }) rectangle-tests , and there exist configurations of m points for which O(m^{2/3}) rectangle-tests are sufficient, whereas to identify a single defective point in the plane, \Theta(m^{1/2}) tests are always necessary and sometimes sufficient.
Edit intent: meaning-changed
Original:  For instance, our results imply that identifying a defective pair in a set of m points in the plane always requires \Omega(m^{ 4 / 7 }) rectangle-tests , and there exist configurations of m points for which O(m^{2/3}) rectangle-tests are sufficient, whereas to identify a single defective point in the plane, \Theta(m^{1/2}) tests are always necessary and sometimes sufficient.
Revised:  For instance, our results imply that identifying a defective pair in a set of m points in the plane always requires \Omega(m^{ 4 / 5 }) rectangle-tests , and there exist configurations of m points for which O(m^{2/3}) rectangle-tests are sufficient, whereas to identify a single defective point in the plane, \Theta(m^{1/2}) tests are always necessary and sometimes sufficient.
Edit intent: meaning-changed
Original:  For instance, our results imply that identifying a defective pair in a set of m points in the plane always requires \Omega(m^{ 4 / 7 }) rectangle-tests , and there exist configurations of m points for which O(m^{2/3}) rectangle-tests are sufficient, whereas to identify a single defective point in the plane, \Theta(m^{1/2}) tests are always necessary and sometimes sufficient.
Revised:  For instance, our results imply that identifying a defective pair in a set of m points in the plane always requires \Omega(m^{ 4 / 7 }) tests , and there exist configurations of m points for which O(m^{2/3}) rectangle-tests are sufficient, whereas to identify a single defective point in the plane, \Theta(m^{1/2}) tests are always necessary and sometimes sufficient.
Edit intent: fluency
Original:  For instance, our results imply that identifying a defective pair in a set of m points in the plane always requires \Omega(m^{ 4 / 7 }) rectangle-tests , and there exist configurations of m points for which O(m^{2/3}) rectangle-tests are sufficient, whereas to identify a single defective point in the plane, \Theta(m^{1/2}) tests are always necessary and sometimes sufficient.
Revised:  For instance, our results imply that identifying a defective pair in a set of m points in the plane always requires \Omega(m^{ 4 / 7 }) rectangle-tests , and there exist configurations of m points for which O(m^{2/3}) tests are sufficient, whereas to identify a single defective point in the plane, \Theta(m^{1/2}) tests are always necessary and sometimes sufficient.
Edit intent: fluency
Original: Current textual question answering models achieve strong performance on in-domain test sets, but often do so by fitting surface-level patterns in the data, so they fail to generalize to out-of-distribution and adversarial settings.
Revised: Current textual question answering models achieve strong performance on in-domain test sets, but often do so by fitting surface-level patterns in the data, so they fail to generalize to out-of-distribution settings.
Edit intent: clarity
Original:  We decompose both the question and context into smaller units based on off-the-shelf semantic representations (here, semantic roles), and solve a subgraph alignment problem to find a part of the context which matches the question.
Revised:  We decompose both the question and context into smaller units based on off-the-shelf semantic representations (here, semantic roles), and align the question to a subgraph of the context which matches the question.
Edit intent: clarity
Original:  We decompose both the question and context into smaller units based on off-the-shelf semantic representations (here, semantic roles), and solve a subgraph alignment problem to find a part of the context which matches the question. Our model uses BERT to compute alignment scores , and by using structured SVM, we can train end-to-end despite complex inference.
Revised:  We decompose both the question and context into smaller units based on off-the-shelf semantic representations (here, semantic roles), and solve a subgraph alignment problem to find a part of the context in order to find the answer. We formulate our model as a structured SVM, with alignment scores computed via BERT, and we can train end-to-end despite complex inference.
Edit intent: clarity
Original:  Furthermore, by investigating differences in scores across different potential answers, we can seek to understand what particular aspects of the input led the model to choose the answer it did without relying on "local" post-hoc explanation techniques.
Revised:  Furthermore, by investigating differences in scores across different potential answers, we can seek to understand what particular aspects of the input lead the model to choose the answer it did without relying on "local" post-hoc explanation techniques.
Edit intent: fluency
Original:  Furthermore, by investigating differences in scores across different potential answers, we can seek to understand what particular aspects of the input led the model to choose the answer it did without relying on "local" post-hoc explanation techniques.
Revised:  Furthermore, by investigating differences in scores across different potential answers, we can seek to understand what particular aspects of the input led the model to choose the answer without relying on "local" post-hoc explanation techniques.
Edit intent: clarity
Original:  Furthermore, by investigating differences in scores across different potential answers, we can seek to understand what particular aspects of the input led the model to choose the answer it did without relying on "local" post-hoc explanation techniques.
Revised:  Furthermore, by investigating differences in scores across different potential answers, we can seek to understand what particular aspects of the input led the model to choose the answer it did without relying on post-hoc explanation techniques.
Edit intent: clarity
Original: In all Countries the political decisions aim at the Covid-19 spreading reduction and at reaching an almost stable configuration of coexistence with the disease, where a small number of new infected individuals per day is sustainable. In such a phase , the containment effort will be reduced in favor of a gradual reopening of the social life and of the various economical sectors and the evaluation of the possible infection regrowth should be very useful.
Revised: In all Countries the political decisions aim at the Covid-19 spreading reduction and at reaching an almost stable configuration of coexistence with the disease, where a small number of new infected individuals per day due to Covid-19. When such a condition is reached , the containment effort will be reduced in favor of a gradual reopening of the social life and of the various economical sectors and the evaluation of the possible infection regrowth should be very useful.
Edit intent: clarity
Original:  In such a phase , the containment effort will be reduced in favor of a gradual reopening of the social life and of the various economical sectors and the evaluation of the possible infection regrowth should be very useful.
Revised:  In such a phase , the containment effort is usually reduced in favor of a gradual reopening of the social life and of the various economical sectors and the evaluation of the possible infection regrowth should be very useful.
Edit intent: clarity
Original:  In such a phase , the containment effort will be reduced in favor of a gradual reopening of the social life and of the various economical sectors and the evaluation of the possible infection regrowth should be very useful. Microscopic models, which take into account the details of the new social and economical conditions, would require a complex dynamics of the stakeholders .
Revised:  In such a phase , the containment effort will be reduced in favor of a gradual reopening of the social life and of the various economical sectors .
Edit intent: coherence
Original:   We discuss a less detailed approach which, on the basis of the collected data  after few days from the beginning of the new phase, outlines different scenarios of the Covid-19 diffusion for longer time.
Revised:  However, in this new phase, the infection spread restarts and a quantitative analysis of the regrowth is very useful. We discuss a less detailed approach which, on the basis of the collected data after few days from the beginning of the new phase, outlines different scenarios of the Covid-19 diffusion for longer time.
Edit intent: meaning-changed
Original:   We discuss a less detailed approach which, on the basis of the collected data  after few days from the beginning of the new phase, outlines different scenarios of the Covid-19 diffusion for longer time.
Revised:  We discuss a macroscopic approach which, on the basis of the collected data after few days from the beginning of the new phase, outlines different scenarios of the Covid-19 diffusion for longer time.
Edit intent: meaning-changed
Original:   We discuss a less detailed approach which, on the basis of the collected data  after few days from the beginning of the new phase, outlines different scenarios of the Covid-19 diffusion for longer time.
Revised:  We discuss a less detailed approach which, on the basis of the collected data in the first lockdown, after few days from the beginning of the new phase, outlines different scenarios of the Covid-19 diffusion for longer time.
Edit intent: meaning-changed
Original:  The  method applies a time dependent carrying capacity, analogously to many macroscopic growth laws in biology, economics and population dynamics.
Revised:  The purpose of this paper is a demonstration-of-concept: one takes simple growth models, considers the available data and shows how the future trend of the spread can be obtained. The method applies a time dependent carrying capacity, analogously to many macroscopic growth laws in biology, economics and population dynamics.
Edit intent: meaning-changed
Original:  The area under the receiver operating characteristic curve is 0.754 for the baseline model and 0.782, 0.785 and 0.789 for multi-task models with border, color and asymmetry respectively.
Revised:  The area under the receiver operating characteristic curve is 0.794 for the baseline model and 0.782, 0.785 and 0.789 for multi-task models with border, color and asymmetry respectively.
Edit intent: meaning-changed
Original:  The area under the receiver operating characteristic curve is 0.754 for the baseline model and 0.782, 0.785 and 0.789 for multi-task models with border, color and asymmetry respectively.
Revised:  The area under the receiver operating characteristic curve is 0.754 for the baseline model and 0.811 and 0.808 for multi-task models with border, color and asymmetry respectively.
Edit intent: meaning-changed
Original:  The area under the receiver operating characteristic curve is 0.754 for the baseline model and 0.782, 0.785 and 0.789 for multi-task models with border, color and asymmetry respectively.
Revised:  The area under the receiver operating characteristic curve is 0.754 for the baseline model and 0.782, 0.785 and 0.789 for multi-task ensembles respectively.
Edit intent: clarity
Original:  We also illustrate how to transform our algorithm into a valid alternative for uni-modal networks as well, and show that it performs at least as well as the standard, modularity-based algorithms -- with a higher numerical performance.
Revised:  We also illustrate how to transform our algorithm into a valid alternative for one-mode networks as well, and show that it performs at least as well as the standard, modularity-based algorithms -- with a higher numerical performance.
Edit intent: meaning-changed
Original: We improve both the open-set generalization and efficiency of link prediction on knowledge graphs by leveraging the contexts of entities and relations in a novel semantic triple encoder. Most previous methods , e.g., translation-based and GCN-based embedding approaches, were built upon graph embedding models.
Revised: Human-curated knowledge graphs provide critical supportive information to various natural language processing tasks, but these graphs are usually incomplete, urging auto-completion of them. Prevalent graph embedding approaches , e.g., translation-based and GCN-based embedding approaches, were built upon graph embedding models.
Edit intent: meaning-changed
Original:  Most previous methods , e.g., translation-based and GCN-based embedding approaches, were built upon graph embedding models. They simply treat the entities/relations as a closed set of graph nodes regardless of their context semantics, which however cannot provide critical information for the generalization to unseen entities/relations. In this paper, we partition each graph triple and develop a novel context-based encoder that separately maps each part and its context into a latent semantic space. We train this semantic triple encoder by optimizing two objectives specifically designed for link prediction. In particular, (1) We split each triple into two parts, i.e. , i) head entity plus relation and ii) tail entity, process both contexts separately by a Transformer encoder, and combine the encoding outputs to derive the prediction.
Revised:  Most previous methods , e.g., TransE, learn structured knowledge via representing graph elements into dense embeddings and capturing their triple-level relationship with spatial distance. However, they are hardly generalizable to the elements never visited in training and are intrinsically vulnerable to graph incompleteness. In contrast, textual encoding approaches, e.g., KG-BERT, resort to graph triple's text and triple-level contextualized representations. They are generalizable enough and robust to the incompleteness, especially when coupled with pre-trained encoders. But two major drawbacks limit the performance: (1) high overheads due to the costly scoring of all possible triples in inference , i) head entity plus relation and ii) tail entity, process both contexts separately by a Transformer encoder, and combine the encoding outputs to derive the prediction.
Edit intent: meaning-changed
Original:  In particular, (1) We split each triple into two parts, i.e. , i) head entity plus relation and ii) tail entity, process both contexts separately by a Transformer encoder, and combine the encoding outputs to derive the prediction. This Siamese-like architecture avoids the combinatorial explosion of candidate triples and significantly improves the efficiency, especially during inference; ( 2) We cover the contextualized semantics of the triples in the encoder so it can handle unseen entities during inference, which promisingly improves the generalization ability;
Revised:  In particular, (1) We split each triple into two parts, i.e. , and ( 2) We cover the contextualized semantics of the triples in the encoder so it can handle unseen entities during inference, which promisingly improves the generalization ability;
Edit intent: coherence
Original:  ( 2) We cover the contextualized semantics of the triples in the encoder so it can handle unseen entities during inference, which promisingly improves the generalization ability; (3) We train the model by optimizing two complementary objectives defined on the triple , i. e., classification and contrastive losses, for natural and reliable ranking scores during inference .
Revised:  ( 2) a lack of structured knowledge in the textual encoder. In this paper, we follow the textual encoding paradigm and aim to alleviate its drawbacks by augmenting it with graph embedding techniques -- a complementary hybrid of both paradigms. Specifically, we partition each triple into two asymmetric parts as in translation-based graph embedding approach, and encode both parts into contextualized representations by a Siamese-style textual encoder. Built upon the representations, our model employs both deterministic classifier and spatial measurement for representation and structure learning respectively. Moreover, we develop a self-adaptive ensemble scheme to further improve the performance by incorporating triple scores from an existing graph embedding model .
Edit intent: meaning-changed
Original:  In experiments, we achieve the state-of-the-art or competitive performance on three popular link predictionbenchmarks. In addition, we empirically reduce the inference costs by one or two orders of magnitude compared to a recent context-based encoding approach and meanwhile keep a superior quality of prediction .
Revised:  In experiments, we achieve the state-of-the-art or competitive performance on three benchmarks and a zero-shot dataset for link prediction, with highlights of inference costs reduced by 1-2 orders of magnitude compared to a recent context-based encoding approach and meanwhile keep a superior quality of prediction .
Edit intent: meaning-changed
Original:  Our dataset involves exploring large knowledge graphs (KG) to retrieve abundant knowledge of various types of main entities  , which makes the current graph-to-sequence models severely suffered from the problems of information loss and parameter explosion while generating the description text .
Revised:  Our dataset involves retrieving abundant knowledge of various types of main entities , which makes the current graph-to-sequence models severely suffered from the problems of information loss and parameter explosion while generating the description text .
Edit intent: clarity
Original:  Our dataset involves exploring large knowledge graphs (KG) to retrieve abundant knowledge of various types of main entities  , which makes the current graph-to-sequence models severely suffered from the problems of information loss and parameter explosion while generating the description text .
Revised:  Our dataset involves exploring large knowledge graphs (KG) to retrieve abundant knowledge of various types of main entities from a large knowledge graph (KG) , which makes the current graph-to-sequence models severely suffered from the problems of information loss and parameter explosion while generating the description text .
Edit intent: meaning-changed
Original:  Our dataset involves exploring large knowledge graphs (KG) to retrieve abundant knowledge of various types of main entities  , which makes the current graph-to-sequence models severely suffered from the problems of information loss and parameter explosion while generating the description text .
Revised:  Our dataset involves exploring large knowledge graphs (KG) to retrieve abundant knowledge of various types of main entities , which makes the current graph-to-sequence models severely suffer from the problems of information loss and parameter explosion while generating the description text .
Edit intent: fluency