wanyu/IteraTeR_full_sent huggingface dataset:
Original: Recent works show that pre-trained masked language models , such as BERT, possess certain linguistic and commonsense knowledge. However, it remains to be seen what types of commonsense knowledge these models have access to.In this vein, we propose to study whether numerical commonsense knowledge -- commonsense knowledge that provides an understanding of the numeric relation between entities -- can be induced from pre-trained masked language models and to what extent is this access to knowledge robust against adversarial examples?
Revised: Recent works show that pre-trained masked language models , such as BERT, possess certain commonsense and factual knowledge. They suggest that it is promising to use PTLMs as "neural knowledge bases" via predicting masked words. Surprisingly, we find that this may not work for numerical commonsense knowledge (e.g., a bird usually has two legs). In this paper, we investigate whether and to what extent is this access to knowledge robust against adversarial examples?
Edit intent: coherence
Original: In this vein, we propose to study whether numerical commonsense knowledge -- commonsense knowledge that provides an understanding of the numeric relation between entities -- can be induced from pre-trained masked language models and to what extent is this access to knowledge robust against adversarial examples?
Revised: In this vein, we propose to study whether numerical commonsense knowledge -- commonsense knowledge that provides an understanding of the numeric relation between entities -- can be induced from pre-trained masked language models and to what extent we can induce numerical commonsense knowledge from PTLMs as well as the robustness of this process.
Edit intent: clarity
Original:  To study this, we introduce a  probing task with a diagnostic dataset, NumerSense, containing 3,145 masked-word-prediction probes . Surprisingly, our experiments and analysis reveal that: (1) BERT and its stronger variant RoBERTa perform poorly on our dataset prior to any fine-tuning;
Revised:  To study this, we introduce a novel probing task with a diagnostic dataset, NumerSense, containing 3,145 masked-word-prediction probes . Surprisingly, our experiments and analysis reveal that: (1) BERT and its stronger variant RoBERTa perform poorly on our dataset prior to any fine-tuning;
Edit intent: clarity
Original:  To study this, we introduce a  probing task with a diagnostic dataset, NumerSense, containing 3,145 masked-word-prediction probes . Surprisingly, our experiments and analysis reveal that: (1) BERT and its stronger variant RoBERTa perform poorly on our dataset prior to any fine-tuning;
Revised:  To study this, we introduce a probing task with a diagnostic dataset, NumerSense, containing 13.6k masked-word-prediction probes . Surprisingly, our experiments and analysis reveal that: (1) BERT and its stronger variant RoBERTa perform poorly on our dataset prior to any fine-tuning;
Edit intent: meaning-changed
Original:  To study this, we introduce a  probing task with a diagnostic dataset, NumerSense, containing 3,145 masked-word-prediction probes . Surprisingly, our experiments and analysis reveal that: (1) BERT and its stronger variant RoBERTa perform poorly on our dataset prior to any fine-tuning;
Revised:  To study this, we introduce a probing task with a diagnostic dataset, NumerSense, containing 3,145 masked-word-prediction probes (10.5k for fine-tuning and 3.1k for testing). Our analysis reveals that: (1) BERT and its stronger variant RoBERTa perform poorly on our dataset prior to any fine-tuning;
Edit intent: meaning-changed
Original:  To study this, we introduce a  probing task with a diagnostic dataset, NumerSense, containing 3,145 masked-word-prediction probes . Surprisingly, our experiments and analysis reveal that: (1) BERT and its stronger variant RoBERTa perform poorly on our dataset prior to any fine-tuning;
Revised:  To study this, we introduce a probing task with a diagnostic dataset, NumerSense, containing 3,145 masked-word-prediction probes . Surprisingly, our experiments and analysis reveal that: (1) BERT and its stronger variant RoBERTa perform poorly on the diagnostic dataset prior to any fine-tuning;
Edit intent: fluency
Original:  OGB datasets are large-scale  , encompass multiple important graph ML tasks  and cover a diverse range of domains, ranging from social and information networks to biological networks, molecular graphs,  and knowledge graphs.
Revised:  OGB datasets are large-scale (up to 100+ million nodes and 1+ billion edges) , encompass multiple important graph ML tasks and cover a diverse range of domains, ranging from social and information networks to biological networks, molecular graphs, and knowledge graphs.
Edit intent: meaning-changed
Original:  OGB datasets are large-scale  , encompass multiple important graph ML tasks  and cover a diverse range of domains, ranging from social and information networks to biological networks, molecular graphs,  and knowledge graphs.
Revised:  OGB datasets are large-scale , encompass multiple important graph ML tasks and cover a diverse range of domains, ranging from social and information networks to biological networks, molecular graphs, source code ASTs, and knowledge graphs.
Edit intent: meaning-changed
Original:  Our empirical investigation reveals the challenges of existing graph methods in handling large-scale graphs and predicting out-of-distribution data. OGB presents an automated end-to-end graph ML pipeline that simplifies and standardizes the process of graph data loading, experimental setup, and model evaluation.
Revised:  Our empirical investigation reveals the challenges of existing graph methods in handling large-scale graphs and predicting out-of-distribution generalization under realistic data splits, indicating fruitful opportunities for future research. Finally, OGB provides an automated end-to-end graph ML pipeline that simplifies and standardizes the process of graph data loading, experimental setup, and model evaluation.
Edit intent: meaning-changed
Original: Commonsense question answering (QA) requires the modeling of general background knowledge about how the world operates and how entities interact with each other . Prior works leveraged manually curated commonsense knowledge graphs to help commonsense reasoningand demonstrated their effectiveness .
Revised: Commonsense question answering (QA) requires background knowledge which is not explicitly stated in a given context . Prior works leveraged manually curated commonsense knowledge graphs to help commonsense reasoningand demonstrated their effectiveness .
Edit intent: meaning-changed
Original: Commonsense question answering (QA) requires the modeling of general background knowledge about how the world operates and how entities interact with each other . Prior works leveraged manually curated commonsense knowledge graphs to help commonsense reasoningand demonstrated their effectiveness .
Revised: Commonsense question answering (QA) requires the modeling of general background knowledge about how the world operates and how entities interact with each other . Prior works use commonsense knowledge graphs to help commonsense reasoningand demonstrated their effectiveness .
Edit intent: clarity
Original: Commonsense question answering (QA) requires the modeling of general background knowledge about how the world operates and how entities interact with each other . Prior works leveraged manually curated commonsense knowledge graphs to help commonsense reasoningand demonstrated their effectiveness .
Revised: Commonsense question answering (QA) requires the modeling of general background knowledge about how the world operates and how entities interact with each other . Prior works leveraged manually curated commonsense knowledge graphs (KGs) to obtain this knowledge for reasoning .
Edit intent: clarity
Original:  In this paper, we propose to learn a multi-hop knowledge path generatorto generate structured evidence dynamically according to the questions.
Revised:  In this paper, we augment a general commonsense QA framework with a knowledgeable path generator. By extrapolating over existing paths in a KG with a state-of-the-art language model, our generator learns to connect a pair of entities in text with a dynamic, and potentially novel, multi-hop knowledge path generatorto generate structured evidence dynamically according to the questions.
Edit intent: meaning-changed
Original:  In this paper, we propose to learn a multi-hop knowledge path generatorto generate structured evidence dynamically according to the questions. Our generator uses a pre-trained language modelas the backbone, leveraging a large amount of unstructured knowledge stored in the language model to supplement the incompleteness of the knowledge base. The experiments on two commonsense QA datasets demonstrate the effectiveness of our method , which improves over strong baselines significantly and also provides human interpretableexplanations for the predictions .
Revised:  In this paper, we propose to learn a multi-hop relational path. Such paths can provide structured evidence for solving commonsense questions without fine-tuning the path generator. Experiments on two datasets show the superiority of our method , which improves over strong baselines significantly and also provides human interpretableexplanations for the predictions .
Edit intent: clarity
Original:  The experiments on two commonsense QA datasets demonstrate the effectiveness of our method , which improves over strong baselines significantly and also provides human interpretableexplanations for the predictions .
Revised:  The experiments on two commonsense QA datasets demonstrate the effectiveness of our method over previous works which fully rely on knowledge from KGs (with up to 6\% improvement in accuracy), across various amounts of training data. Further evaluation suggests that the generated paths are typically interpretable, novel, and relevant to the task .
Edit intent: meaning-changed
Original: Cross-lingual Entity Linking (XEL) grounds mentions of entities that appear in a foreign (source) language text into an English (target) knowledge base (KB) such as Wikipedia . XEL consists of two steps: candidate generation, which retrieves a list of candidate entities for each mention, followed by candidate ranking.
Revised: Cross-lingual Entity Linking (XEL) , the problem of grounding mentions of entities that appear in a foreign (source) language text into an English (target) knowledge base (KB) such as Wikipedia . XEL consists of two steps: candidate generation, which retrieves a list of candidate entities for each mention, followed by candidate ranking.
Edit intent: coherence
Original: Cross-lingual Entity Linking (XEL) grounds mentions of entities that appear in a foreign (source) language text into an English (target) knowledge base (KB) such as Wikipedia . XEL consists of two steps: candidate generation, which retrieves a list of candidate entities for each mention, followed by candidate ranking.
Revised: Cross-lingual Entity Linking (XEL) grounds mentions of entities in a foreign (source) language text into an English (target) knowledge base (KB) such as Wikipedia . XEL consists of two steps: candidate generation, which retrieves a list of candidate entities for each mention, followed by candidate ranking.
Edit intent: clarity
Original: Cross-lingual Entity Linking (XEL) grounds mentions of entities that appear in a foreign (source) language text into an English (target) knowledge base (KB) such as Wikipedia . XEL consists of two steps: candidate generation, which retrieves a list of candidate entities for each mention, followed by candidate ranking.
Revised: Cross-lingual Entity Linking (XEL) grounds mentions of entities that appear in a foreign language text into an English (target) knowledge base (KB) such as Wikipedia . XEL consists of two steps: candidate generation, which retrieves a list of candidate entities for each mention, followed by candidate ranking.
Edit intent: clarity
Original: Cross-lingual Entity Linking (XEL) grounds mentions of entities that appear in a foreign (source) language text into an English (target) knowledge base (KB) such as Wikipedia . XEL consists of two steps: candidate generation, which retrieves a list of candidate entities for each mention, followed by candidate ranking.
Revised: Cross-lingual Entity Linking (XEL) grounds mentions of entities that appear in a foreign (source) language text into an English knowledge base such as Wikipedia . XEL consists of two steps: candidate generation, which retrieves a list of candidate entities for each mention, followed by candidate ranking.
Edit intent: clarity
Original: Cross-lingual Entity Linking (XEL) grounds mentions of entities that appear in a foreign (source) language text into an English (target) knowledge base (KB) such as Wikipedia . XEL consists of two steps: candidate generation, which retrieves a list of candidate entities for each mention, followed by candidate ranking. XEL methods have been successful on high-resource languages, but generally perform poorly on low-resource languages due to lack of supervision.
Revised: Cross-lingual Entity Linking (XEL) grounds mentions of entities that appear in a foreign (source) language text into an English (target) knowledge base (KB) such as Wikipedia , has seen a lot of research in recent years, with a range of promising techniques. However, current techniques do not rise to the challenges introduced by text in low-resource languages due to lack of supervision.
Edit intent: clarity
Original:  XEL methods have been successful on high-resource languages, but generally perform poorly on low-resource languages due to lack of supervision. In this paper , we show a thorough analysis on existing low-resource XEL methods, especially on their candidate generation methods and limitations.
Revised:  XEL methods have been successful on high-resource languages, but generally perform poorly on low-resource languages (LRL) and, surprisingly, fail to generalize to text not taken from Wikipedia, on which they are usually trained. This paper provides a thorough analysis on existing low-resource XEL methods, especially on their candidate generation methods and limitations.
Edit intent: meaning-changed
Original:  In this paper , we show a thorough analysis on existing low-resource XEL methods, especially on their candidate generation methods and limitations. We observed several interesting findings: 1. They are heavily limited by the Wikipedia bilingual resource coverage.
Revised:  In this paper , we show a thorough analysis on existing low-resource XEL techniques, focusing on the key step of identifying candidate English Wikipedia titles that correspond to a given foreign language mention. Our analysis indicates that current methods are limited by their reliance on Wikipedia's interlanguage links and thus suffer when the foreign language's Wikipedia is small. We conclude that the Wikipedia bilingual resource coverage.
Edit intent: meaning-changed
Original:  In this paper, we claim that , under the low-resource language setting , outside-Wikipedia cross-lingual resources are essential. To prove this argument, we propose a simple but effective zero-shot framework, CogCompXEL, that complements current methods by utilizing query log mapping files from online search engines . CogCompXEL outperforms current state-of-the-art models on almost all 25 languages of the LORELEI dataset, achieving an absolute average increase of 25 \% in gold candidate recall.
Revised:  In this paper, we claim that , under the low-resource language setting , outside-Wikipedia cross-lingual resources and present a simple yet effective zero-shot framework, CogCompXEL, that complements current methods by utilizing query log mapping files from online search engines . CogCompXEL outperforms current state-of-the-art models on almost all 25 languages of the LORELEI dataset, achieving an absolute average increase of 25 \% in gold candidate recall.
Edit intent: coherence
Original:  To prove this argument, we propose a simple but effective zero-shot framework, CogCompXEL, that complements current methods by utilizing query log mapping files from online search engines . CogCompXEL outperforms current state-of-the-art models on almost all 25 languages of the LORELEI dataset, achieving an absolute average increase of 25 \% in gold candidate recall.
Revised:  To prove this argument, we propose a simple but effective zero-shot XEL system, QuEL, that utilizes search engines query logs. With experiments on 25 languages of the LORELEI dataset, achieving an absolute average increase of 25 \% in gold candidate recall.
Edit intent: clarity
Original:  To prove this argument, we propose a simple but effective zero-shot framework, CogCompXEL, that complements current methods by utilizing query log mapping files from online search engines . CogCompXEL outperforms current state-of-the-art models on almost all 25 languages of the LORELEI dataset, achieving an absolute average increase of 25 \% in gold candidate recall.
Revised:  To prove this argument, we propose a simple but effective zero-shot framework, CogCompXEL, that complements current methods by utilizing query log mapping files from online search engines . CogCompXEL outperforms current state-of-the-art models on almost all 25 languages , QuEL~shows an average increase of 25 \% in gold candidate recall.
Edit intent: meaning-changed
Original:  To prove this argument, we propose a simple but effective zero-shot framework, CogCompXEL, that complements current methods by utilizing query log mapping files from online search engines . CogCompXEL outperforms current state-of-the-art models on almost all 25 languages of the LORELEI dataset, achieving an absolute average increase of 25 \% in gold candidate recall.
Revised:  To prove this argument, we propose a simple but effective zero-shot framework, CogCompXEL, that complements current methods by utilizing query log mapping files from online search engines . CogCompXEL outperforms current state-of-the-art models on almost all 25 languages of the LORELEI dataset, achieving an absolute average increase of 25 
Edit intent: clarity
Original:  We show the validity of the proposed approach across different data sampling strategies on six public datasets and demonstrate that the self-attention mechanism gave  significant improvement in performance over deep networks using a combination of recurrent and convolution networks.
Revised:  We show the validity of the proposed approach across different data sampling strategies on six public datasets and demonstrate that the self-attention mechanism gave a significant improvement in performance over deep networks using a combination of recurrent and convolution networks.
Edit intent: fluency
Original: Procedural knowledge, which we define as concrete information about the sequence of actions that go into performing a particular procedure, plays an important role in understanding real-world tasks and actions. Humans often learn this knowledge from instructional text and video, and in this paper we aim to perform automatic extraction of this knowledge in a similar way. As a concrete step in this direction, we propose the new task of inferring procedures in a structured form (a data structure containing verbs and arguments) from multimodal instructional video contents and their corresponding transcripts. We first create a manually annotated , large evaluation dataset including over350 instructional cooking videos along with over 15, 000 English sentences in transcripts spanning over 89 recipes.
Revised: Watching instructional videos are often used to learn about procedures. Video captioning is one way of automatically collecting such knowledge. However, it provides only an indirect, overall evaluation of multimodal models with no finer-grained quantitative measure of what they have learned. We propose instead, a benchmark of structured procedural knowledge extracted from cooking videos. This work is complementary to existing tasks, but requires models to produce interpretable structured knowledge in the form of verb-argument tuples. Our manually annotated open-vocabulary resource includes 356 instructional cooking videos along with over 15, 000 English sentences in transcripts spanning over 89 recipes.
Edit intent: clarity
Original:  We first create a manually annotated , large evaluation dataset including over350 instructional cooking videos along with over 15, 000 English sentences in transcripts spanning over 89 recipes. We conduct analysis of the challenges posed by this task and dataset with experiments with unsupervised segmentation, semantic role labeling, and visual action detection based baselines.
Revised:  We first create a manually annotated , large evaluation dataset including over350 instructional cooking videos along with over 15, 523 video clip/sentence-level annotations. Our analysis shows that the proposed task is challenging and standard modeling approaches like unsupervised segmentation, semantic role labeling, and visual action detection based baselines.
Edit intent: meaning-changed
Original:  We conduct analysis of the challenges posed by this task and dataset with experiments with unsupervised segmentation, semantic role labeling, and visual action detection based baselines.The dataset and code will be publicly available at URL
Revised:  We conduct analysis of the challenges posed by this task and dataset with experiments with unsupervised segmentation, semantic role labeling, and visual action detection perform poorly when forced to predict every action of a procedure in a structured form.
Edit intent: meaning-changed
Original:   Typical scenarios are explored using UK Office of National Statistics life tables , showing the behaviour of these characteristics under varying degrees of constant relative risk aversion .
Revised:  I show that under certain circumstances, such as a very high bequest motive, a life-cycle utility maximisation strategy will necessitate negative mortality credits analogous to a member paying life insurance premiums. Typical scenarios are explored using UK Office of National Statistics life tables , showing the behaviour of these characteristics under varying degrees of constant relative risk aversion .
Edit intent: meaning-changed
Original:  However, to what extent is the information encoded in a sentence representation actually used for the task which the encoder is trained on ? In this work, we examine this probing paradigm through a case-study in Natural Language Inference, showing that models  learn to encode linguistic properties even when not needed for a task .
Revised:  However, to what extent was the information encoded in a sentence representation actually used for the task which the encoder is trained on ? In this work, we examine this probing paradigm through a case-study in Natural Language Inference, showing that models learn to encode linguistic properties even when not needed for a task .
Edit intent: fluency
Original:  However, to what extent is the information encoded in a sentence representation actually used for the task which the encoder is trained on ? In this work, we examine this probing paradigm through a case-study in Natural Language Inference, showing that models  learn to encode linguistic properties even when not needed for a task .
Revised:  However, to what extent is the information encoded in sentence representations, as discovered through a probe, actually used by the model to perform its task ? In this work, we examine this probing paradigm through a case-study in Natural Language Inference, showing that models learn to encode linguistic properties even when not needed for a task .
Edit intent: clarity