budecosystem/superglue_ax_b huggingface dataset:
Premise: This gives the model a sense of the implied action dynamics of the verb between the agent and the world.
Hypothesis: This gives to the model a sense of the implied action dynamics of the verb between the agent and the world.
Label: entailment
Predicate-argument structure: Datives
Premise: This gives to the model a sense of the implied action dynamics of the verb between the agent and the world.
Hypothesis: This gives the model a sense of the implied action dynamics of the verb between the agent and the world.
Label: entailment
Predicate-argument structure: Datives
Premise: This gives the model a sense of the implied action dynamics of the verb between the agent and the world.
Hypothesis: This gives the model to a sense of the implied action dynamics of the verb between the agent and the world.
Label: not_entailment
Predicate-argument structure: Datives
Premise: This gives the model to a sense of the implied action dynamics of the verb between the agent and the world.
Hypothesis: This gives the model a sense of the implied action dynamics of the verb between the agent and the world.
Label: not_entailment
Predicate-argument structure: Datives
Premise: This attribute group specifies prominent body parts involved in carrying out the action.
Hypothesis: This attribute group specifies prominent limbs involved in carrying out the action.
Label: entailment
Lexical semantics: Lexical entailment
Premise: This attribute group specifies prominent limbs involved in carrying out the action.
Hypothesis: This attribute group specifies prominent body parts involved in carrying out the action.
Label: not_entailment
Lexical semantics: Lexical entailment
Premise: This problem has been studied before for zero-shot object recognition, but there are several key differences.
Hypothesis: This problem has been previously studied for zero-shot object recognition, but there are several key differences.
Label: entailment
Logic: Temporal
Premise: This problem has been previously studied for zero-shot object recognition, but there are several key differences.
Hypothesis: This problem has been studied before for zero-shot object recognition, but there are several key differences.
Label: entailment
Logic: Temporal
Premise: This problem has been studied before for zero-shot object recognition, but there are several key differences.
Hypothesis: This problem will be studied for zero-shot object recognition, but there are several key differences.
Label: not_entailment
Logic: Temporal
Premise: This problem will be studied for zero-shot object recognition, but there are several key differences.
Hypothesis: This problem has been studied before for zero-shot object recognition, but there are several key differences.
Label: not_entailment
Logic: Temporal
Premise: Understanding a long document requires tracking how entities are introduced and evolve over time.
Hypothesis: Understanding a long document requires evolving over time.
Label: not_entailment
Predicate-argument structure: Coordination scope
Premise: Understanding a long document requires evolving over time.
Hypothesis: Understanding a long document requires tracking how entities are introduced and evolve over time.
Label: not_entailment
Predicate-argument structure: Coordination scope
Premise: Understanding a long document requires tracking how entities are introduced and evolve over time.
Hypothesis: Understanding a long document requires tracking how entities evolve over time.
Label: entailment
Predicate-argument structure: Coordination scope
Premise: Understanding a long document requires tracking how entities evolve over time.
Hypothesis: Understanding a long document requires tracking how entities are introduced and evolve over time.
Label: not_entailment
Predicate-argument structure: Coordination scope
Premise: Understanding a long document requires tracking how entities are introduced and evolve over time.
Hypothesis: Understanding a long document requires understanding how entities are introduced.
Label: entailment
Logic: Conjunction;Upward monotone
Premise: Understanding a long document requires understanding how entities are introduced.
Hypothesis: Understanding a long document requires tracking how entities are introduced and evolve over time.
Label: not_entailment
Logic: Conjunction;Upward monotone
Premise: We do not assume that these variables are observed at test time.
Hypothesis: These variables are not observed at test time.
Label: not_entailment
Lexical semantics: Factivity
Premise: These variables are not observed at test time.
Hypothesis: We do not assume that these variables are observed at test time.
Label: not_entailment
Lexical semantics: Factivity
Premise: To compute the perplexity numbers on the test data, our model only takes account of log probabilities on word prediction.
Hypothesis: To compute the perplexity numbers on the test data, our model doesn't take account of anything other than the log probabilities on word prediction.
Label: entailment
Logic: Double negation
Premise: To compute the perplexity numbers on the test data, our model doesn't take account of anything other than the log probabilities on word prediction.
Hypothesis: To compute the perplexity numbers on the test data, our model only takes account of log probabilities on word prediction.
Label: entailment
Logic: Double negation
Premise: We also experiment with the option to either use the pretrained GloVe word embeddings or randomly initialized word embeddings (then updated during training).
Hypothesis: We experiment with the option using randomly initialized word embeddings (then updated during training).
Label: entailment
Logic: Disjunction
Premise: We experiment with the option using randomly initialized word embeddings (then updated during training).
Hypothesis: We also experiment with the option to either use the pretrained GloVe word embeddings or randomly initialized word embeddings (then updated during training).
Label: not_entailment
Logic: Disjunction
Premise: The entity prediction task requires predicting xxxx given the preceding text either by choosing a previously mentioned entity or deciding that this is a “new entity”.
Hypothesis: The entity prediction task requires predicting xxxx given the preceding text by choosing a previously mentioned entity.
Label: not_entailment
Logic: Disjunction;Non-monotone
Premise: The entity prediction task requires predicting xxxx given the preceding text by choosing a previously mentioned entity.
Hypothesis: The entity prediction task requires predicting xxxx given the preceding text either by choosing a previously mentioned entity or deciding that this is a “new entity”.
Label: not_entailment
Logic: Disjunction;Non-monotone
Premise: So there is no dedicated memory block for every entity and no distinction between entity mentions and non-mention words.
Hypothesis: So there is no dedicated high-dimensional memory block for every entity and no distinction between entity mentions and non-mention words.
Label: entailment
Logic: Downward monotone
Predicate-argument structure: Intersectivity
Premise: So there is no dedicated high-dimensional memory block for every entity and no distinction between entity mentions and non-mention words.
Hypothesis: So there is no dedicated memory block for every entity and no distinction between entity mentions and non-mention words.
Label: not_entailment
Logic: Downward monotone
Predicate-argument structure: Intersectivity
Premise: Our approach complements these previous methods.
Hypothesis: Our approach complements some previous methods.
Label: entailment
Logic: Existential
Premise: Our approach complements some previous methods.
Hypothesis: Our approach complements these previous methods.
Label: not_entailment
Logic: Existential
Premise: We manually annotated 687 templates mapping KB predicates to text for different compositionality types (with 462 unique KB predicates), and use those templates to modify the original WebQuestionsSP question according to the meaning of the generated SPARQL query.
Hypothesis: We manually annotated over 650 templates mapping KB predicates to text for different compositionality types (with 462 unique KB predicates), and use those templates to modify the original WebQuestionsSP question according to the meaning of the generated SPARQL query.
Label: entailment
Logic: Intervals/Numbers
Premise: We manually annotated over 650 templates mapping KB predicates to text for different compositionality types (with 462 unique KB predicates), and use those templates to modify the original WebQuestionsSP question according to the meaning of the generated SPARQL query.
Hypothesis: We manually annotated 687 templates mapping KB predicates to text for different compositionality types (with 462 unique KB predicates), and use those templates to modify the original WebQuestionsSP question according to the meaning of the generated SPARQL query.
Label: not_entailment
Logic: Intervals/Numbers
Premise: We manually annotated 687 templates mapping KB predicates to text for different compositionality types (with 462 unique KB predicates), and use those templates to modify the original WebQuestionsSP question according to the meaning of the generated SPARQL query.
Hypothesis: We manually annotated over 690 templates mapping KB predicates to text for different compositionality types (with 462 unique KB predicates), and use those templates to modify the original WebQuestionsSP question according to the meaning of the generated SPARQL query.
Label: not_entailment
Logic: Intervals/Numbers
Premise: We manually annotated over 690 templates mapping KB predicates to text for different compositionality types (with 462 unique KB predicates), and use those templates to modify the original WebQuestionsSP question according to the meaning of the generated SPARQL query.
Hypothesis: We manually annotated 687 templates mapping KB predicates to text for different compositionality types (with 462 unique KB predicates), and use those templates to modify the original WebQuestionsSP question according to the meaning of the generated SPARQL query.
Label: not_entailment
Logic: Intervals/Numbers
Premise: To generate diversity, workers got a bonus if the edit distance of a paraphrase was high compared to the MG question.
Hypothesis: To generate diversity, workers whose paraphrases had high edit distance compared to the MG question got a bonus.
Label: entailment
Logic: Conditionals
Predicate-argument structure: Relative clauses;Restrictivity
Premise: To generate diversity, workers whose paraphrases had high edit distance compared to the MG question got a bonus.
Hypothesis: To generate diversity, workers got a bonus if the edit distance of a paraphrase was high compared to the MG question.
Label: entailment
Logic: Conditionals
Predicate-argument structure: Relative clauses;Restrictivity
Premise: To generate diversity, workers got a bonus if the edit distance of a paraphrase was high compared to the MG question.
Hypothesis: To generate diversity, workers got a bonus if the edit distance of a paraphrase was above 3 operations compared to the MG question.
Label: not_entailment
Logic: Conditionals
Predicate-argument structure: Relative clauses;Restrictivity
Premise: To generate diversity, workers got a bonus if the edit distance of a paraphrase was above 3 operations compared to the MG question.
Hypothesis: To generate diversity, workers got a bonus if the edit distance of a paraphrase was high compared to the MG question.
Label: not_entailment
Logic: Conditionals
Predicate-argument structure: Relative clauses;Restrictivity
Premise: To generate complex questions we use the dataset WEBQUESTIONSSP, which contains 4,737 questions paired with SPARQL queries for Freebase.
Hypothesis: To generate simple questions we use the dataset WEBQUESTIONSSP, which contains 4,737 questions paired with SPARQL queries for Freebase.
Label: not_entailment
Lexical semantics: Lexical entailment
Premise: To generate simple questions we use the dataset WEBQUESTIONSSP, which contains 4,737 questions paired with SPARQL queries for Freebase.
Hypothesis: To generate complex questions we use the dataset WEBQUESTIONSSP, which contains 4,737 questions paired with SPARQL queries for Freebase.
Label: not_entailment
Lexical semantics: Lexical entailment
Premise: To generate complex questions we use the dataset WEBQUESTIONSSP, which contains 4,737 questions paired with SPARQL queries for Freebase.
Hypothesis: To generate highly compositional questions we use the dataset WEBQUESTIONSSP, which contains 4,737 questions paired with SPARQL queries for Freebase.
Label: entailment
Knowledge: World knowledge
Premise: To generate highly compositional questions we use the dataset WEBQUESTIONSSP, which contains 4,737 questions paired with SPARQL queries for Freebase.
Hypothesis: To generate complex questions we use the dataset WEBQUESTIONSSP, which contains 4,737 questions paired with SPARQL queries for Freebase.
Label: entailment
Knowledge: World knowledge
Premise: In this paper, we explore the idea of polyglot semantic translation, or learning semantic parsing models that are trained on multiple datasets and natural languages.
Hypothesis: In this paper, we explore the idea of learning semantic parsing models that are trained on multiple datasets and natural languages.
Label: entailment
Predicate-argument structure: Restrictivity
Knowledge: World knowledge
Premise: In this paper, we explore the idea of learning semantic parsing models that are trained on multiple datasets and natural languages.
Hypothesis: In this paper, we explore the idea of polyglot semantic translation, or learning semantic parsing models that are trained on multiple datasets and natural languages.
Label: entailment
Predicate-argument structure: Restrictivity
Knowledge: World knowledge
Premise: They then use a discriminative model to rerank the translation output using additional nonworld level features.
Hypothesis: They then use a generative model to rerank the translation output using additional nonworld level features.
Label: not_entailment
Lexical semantics: Lexical entailment
Premise: They then use a generative model to rerank the translation output using additional nonworld level features.
Hypothesis: They then use a discriminative model to rerank the translation output using additional nonworld level features.
Label: not_entailment
Lexical semantics: Lexical entailment
Premise: In contrast to standard MT tasks, we are dealing with a relatively low-resource setting where the sparseness of the target vocabulary is an issue.
Hypothesis: Unlike in standard MT tasks, we are dealing with a relatively low-resource setting where the sparseness of the target vocabulary is an issue.
Label: entailment
Lexical semantics: Lexical entailment
Premise: Unlike in standard MT tasks, we are dealing with a relatively low-resource setting where the sparseness of the target vocabulary is an issue.
Hypothesis: In contrast to standard MT tasks, we are dealing with a relatively low-resource setting where the sparseness of the target vocabulary is an issue.
Label: entailment
Lexical semantics: Lexical entailment
Premise: A distribution is then computed over these actions using a softmax function and particular actions are chosen accordingly during training and decoding.
Hypothesis: Logits are then computed for these actions and particular actions are chosen according to a softmax over these logits during training and decoding.
Label: entailment
Knowledge: World knowledge
Premise: Logits are then computed for these actions and particular actions are chosen according to a softmax over these logits during training and decoding.
Hypothesis: A distribution is then computed over these actions using a softmax function and particular actions are chosen accordingly during training and decoding.
Label: entailment
Knowledge: World knowledge
Premise: A distribution is then computed over these actions using a softmax function and particular actions are chosen accordingly during training and decoding.
Hypothesis: A distribution is then computed over these actions using a maximum-entropy approach and particular actions are chosen accordingly during training and decoding.
Label: entailment
Knowledge: World knowledge
Premise: A distribution is then computed over these actions using a maximum-entropy approach and particular actions are chosen accordingly during training and decoding.
Hypothesis: A distribution is then computed over these actions using a softmax function and particular actions are chosen accordingly during training and decoding.
Label: not_entailment
Knowledge: World knowledge
Premise: A distribution is then computed over these actions using a softmax function and particular actions are chosen accordingly during training and decoding.
Hypothesis: A distribution is then computed over these actions using a softmax function and particular actions are chosen randomly during training and decoding.
Label: not_entailment
Lexical semantics: Lexical entailment
Premise: A distribution is then computed over these actions using a softmax function and particular actions are chosen randomly during training and decoding.
Hypothesis: A distribution is then computed over these actions using a softmax function and particular actions are chosen accordingly during training and decoding.
Label: not_entailment
Lexical semantics: Lexical entailment
Premise: The systems thus produced are incremental: dialogues are processed word-by-word, shown previously to be essential in supporting natural, spontaneous dialogue.
Hypothesis: The systems thus produced support the capability to interrupt an interlocutor mid-sentence.
Label: entailment
Knowledge: Common sense
Premise: The systems thus produced support the capability to interrupt an interlocutor mid-sentence.
Hypothesis: The systems thus produced are incremental: dialogues are processed word-by-word, shown previously to be essential in supporting natural, spontaneous dialogue.
Label: not_entailment
Knowledge: Common sense
Premise: The systems thus produced are incremental: dialogues are processed word-by-word, shown previously to be essential in supporting natural, spontaneous dialogue.
Hypothesis: The systems thus produced are incremental: dialogues are processed sentence-by-sentence, shown previously to be essential in supporting natural, spontaneous dialogue.
Label: not_entailment
Lexical semantics: Lexical entailment
Premise: The systems thus produced are incremental: dialogues are processed sentence-by-sentence, shown previously to be essential in supporting natural, spontaneous dialogue.
Hypothesis: The systems thus produced are incremental: dialogues are processed word-by-word, shown previously to be essential in supporting natural, spontaneous dialogue.
Label: not_entailment
Lexical semantics: Lexical entailment
Premise: Indeed, it is often stated that for humans to learn how to perform adequately in a domain, one example is enough from which to learn.
Hypothesis: Indeed, it is often stated that for humans to learn how to perform adequately in a domain, one-shot learning is sufficient.
Label: entailment
Knowledge: World knowledge
Premise: Indeed, it is often stated that for humans to learn how to perform adequately in a domain, one-shot learning is sufficient.
Hypothesis: Indeed, it is often stated that for humans to learn how to perform adequately in a domain, one example is enough from which to learn.
Label: entailment
Knowledge: World knowledge
Premise: Indeed, it is often stated that for humans to learn how to perform adequately in a domain, one example is enough from which to learn.
Hypothesis: Indeed, it is often stated that for humans to learn how to perform adequately in a domain, any number of examples is enough from which to learn.
Label: not_entailment
Logic: Upward monotone
Premise: Indeed, it is often stated that for humans to learn how to perform adequately in a domain, any number of examples is enough from which to learn.
Hypothesis: Indeed, it is often stated that for humans to learn how to perform adequately in a domain, one example is enough from which to learn.
Label: not_entailment
Logic: Upward monotone
Premise: We investigate a wide range of metrics, including state-of-the-art word-based and novel grammar-based ones, and demonstrate that they only weakly reflect human judgements of system outputs as generated by data-driven, end-to-end NLG.
Hypothesis: We investigate a wide range of metrics, including state-of-the-art word-based and novel grammar-based ones, and demonstrate that they only weakly reflect human judgements of system outputs as generated by data-driven, end-to-end natural language generation.
Label: entailment
Lexical semantics: Named entities
Knowledge: World knowledge
Premise: We investigate a wide range of metrics, including state-of-the-art word-based and novel grammar-based ones, and demonstrate that they only weakly reflect human judgements of system outputs as generated by data-driven, end-to-end natural language generation.
Hypothesis: We investigate a wide range of metrics, including state-of-the-art word-based and novel grammar-based ones, and demonstrate that they only weakly reflect human judgements of system outputs as generated by data-driven, end-to-end NLG.
Label: entailment
Lexical semantics: Named entities
Knowledge: World knowledge
Premise: We investigate a wide range of metrics, including state-of-the-art word-based and novel grammar-based ones, and demonstrate that they only weakly reflect human judgements of system outputs as generated by data-driven, end-to-end NLG.
Hypothesis: We investigate a wide range of metrics, including state-of-the-art word-based and novel grammar-based ones, and demonstrate that they only weakly reflect human judgements of system outputs as generated by data-driven, end-to-end natural language parsing.
Label: not_entailment
Lexical semantics: Named entities
Knowledge: World knowledge
Premise: We investigate a wide range of metrics, including state-of-the-art word-based and novel grammar-based ones, and demonstrate that they only weakly reflect human judgements of system outputs as generated by data-driven, end-to-end natural language parsing.
Hypothesis: We investigate a wide range of metrics, including state-of-the-art word-based and novel grammar-based ones, and demonstrate that they only weakly reflect human judgements of system outputs as generated by data-driven, end-to-end NLG.
Label: not_entailment
Lexical semantics: Named entities
Knowledge: World knowledge
Premise: To assess the reliability of ratings, we calculated the intra-class correlation coefficient (ICC), which measures inter-observer reliability on ordinal data for more than two raters (Landis and Koch, 1977).
Hypothesis: To assess the unreliability of ratings, we calculated the intra-class correlation coefficient (ICC), which measures inter-observer reliability on ordinal data for more than two raters (Landis and Koch, 1977).
Label: entailment
Lexical semantics: Morphological negation
Knowledge: Common sense
Premise: To assess the unreliability of ratings, we calculated the intra-class correlation coefficient (ICC), which measures inter-observer reliability on ordinal data for more than two raters (Landis and Koch, 1977).
Hypothesis: To assess the reliability of ratings, we calculated the intra-class correlation coefficient (ICC), which measures inter-observer reliability on ordinal data for more than two raters (Landis and Koch, 1977).
Label: entailment
Lexical semantics: Morphological negation
Knowledge: Common sense
Premise: We also show that metric performance is data- and system-specific.
Hypothesis: We also show that metric performance varies between datasets and systems.
Label: entailment
Knowledge: Common sense
Premise: We also show that metric performance varies between datasets and systems.
Hypothesis: We also show that metric performance is data- and system-specific.
Label: entailment
Knowledge: Common sense
Premise: We also show that metric performance is data- and system-specific.
Hypothesis: We also show that metric performance is constant between datasets and systems.
Label: not_entailment
Knowledge: Common sense
Premise: We also show that metric performance is constant between datasets and systems.
Hypothesis: We also show that metric performance is data- and system-specific.
Label: not_entailment
Knowledge: Common sense
Premise: Our experiments indicate that neural systems are quite good at producing fluent outputs and generally score well on standard word-match metrics, but perform quite poorly at content selection and at capturing long-term structure.
Hypothesis: Our experiments indicate that neural systems are quite good at surface-level language modeling, but perform quite poorly at capturing higher level semantics and structure.
Label: entailment
Knowledge: World knowledge
Premise: Our experiments indicate that neural systems are quite good at surface-level language modeling, but perform quite poorly at capturing higher level semantics and structure.
Hypothesis: Our experiments indicate that neural systems are quite good at producing fluent outputs and generally score well on standard word-match metrics, but perform quite poorly at content selection and at capturing long-term structure.
Label: entailment
Knowledge: World knowledge
Premise: Our experiments indicate that neural systems are quite good at producing fluent outputs and generally score well on standard word-match metrics, but perform quite poorly at content selection and at capturing long-term structure.
Hypothesis: Our experiments indicate that neural systems are quite good at capturing higher level semantics and structure but perform quite poorly at surface-level language modeling.
Label: not_entailment
Knowledge: World knowledge
Premise: Our experiments indicate that neural systems are quite good at capturing higher level semantics and structure but perform quite poorly at surface-level language modeling.
Hypothesis: Our experiments indicate that neural systems are quite good at producing fluent outputs and generally score well on standard word-match metrics, but perform quite poorly at content selection and at capturing long-term structure.
Label: not_entailment
Knowledge: World knowledge
Premise: Reconstruction-based techniques can also be applied at the document or sentence-level during training.
Hypothesis: Reconstruction-based techniques can operate on multiple scales during training.
Label: entailment
Knowledge: Common sense
Premise: Reconstruction-based techniques can operate on multiple scales during training.
Hypothesis: Reconstruction-based techniques can also be applied at the document or sentence-level during training.
Label: entailment
Knowledge: Common sense
Premise: Reconstruction-based techniques can also be applied at the document or sentence-level during training.
Hypothesis: Reconstruction-based techniques can also be applied at the document or sentence-level during test.
Label: not_entailment
Knowledge: Common sense
Premise: Reconstruction-based techniques can also be applied at the document or sentence-level during test.
Hypothesis: Reconstruction-based techniques can also be applied at the document or sentence-level during training.
Label: not_entailment
Knowledge: Common sense
Premise: Reconstruction-based techniques can also be applied at the document or sentence-level during training.
Hypothesis: Reconstruction-based techniques can only be applied at the sentence-level during training.
Label: not_entailment
Logic: Disjunction
Premise: Reconstruction-based techniques can only be applied at the sentence-level during training.
Hypothesis: Reconstruction-based techniques can also be applied at the document or sentence-level during training.
Label: not_entailment
Logic: Disjunction
Premise: In practice, our proposed extractive evaluation will pick up on many errors in this passage.
Hypothesis: In practice, our proposed extractive evaluation will pick up on few errors in this passage.
Label: not_entailment
Lexical semantics: Lexical entailment;Quantifiers
Premise: In practice, our proposed extractive evaluation will pick up on few errors in this passage.
Hypothesis: In practice, our proposed extractive evaluation will pick up on many errors in this passage.
Label: not_entailment
Lexical semantics: Lexical entailment;Quantifiers
Premise: Similarly, the use of more agent-empowering verbs in female narratives decrease the odds of passing the Bechdel test.
Hypothesis: Similarly, the use of more agent-empowering verbs in female narratives decrease the odds of two named women characters talking about something besides men.
Label: entailment
Knowledge: World knowledge
Premise: Similarly, the use of more agent-empowering verbs in female narratives decrease the odds of two named women characters talking about something besides men.
Hypothesis: Similarly, the use of more agent-empowering verbs in female narratives decrease the odds of passing the Bechdel test.
Label: entailment
Knowledge: World knowledge
Premise: Similarly, the use of more agent-empowering verbs in female narratives decrease the odds of passing the Bechdel test.
Hypothesis: Similarly, the use of more agent-empowering verbs in female narratives decrease the odds of men in the narrative talking to each other about women.
Label: not_entailment
Knowledge: World knowledge
Premise: Similarly, the use of more agent-empowering verbs in female narratives decrease the odds of men in the narrative talking to each other about women.
Hypothesis: Similarly, the use of more agent-empowering verbs in female narratives decrease the odds of passing the Bechdel test.
Label: not_entailment
Knowledge: World knowledge
Premise: Furthermore, male characters use inhibitory language more (inhib), which contains words pertaining to blocking or allowing, suggesting that these characters are in positions of power.
Hypothesis: Furthermore, male characters use inhibitory language more (inhib), which contains words pertaining to blocking or allowing, suggesting that these characters are more often in positions where they can forbid or permit actions and decisions.
Label: entailment
Knowledge: Common sense
Premise: Furthermore, male characters use inhibitory language more (inhib), which contains words pertaining to blocking or allowing, suggesting that these characters are more often in positions where they can forbid or permit actions and decisions.
Hypothesis: Furthermore, male characters use inhibitory language more (inhib), which contains words pertaining to blocking or allowing, suggesting that these characters are in positions of power.
Label: entailment
Knowledge: Common sense
Premise: Furthermore, male characters use inhibitory language more (inhib), which contains words pertaining to blocking or allowing, suggesting that these characters are in positions of power.
Hypothesis: Furthermore, male characters use inhibitory language more (inhib), which contains words pertaining to blocking or allowing, suggesting that these characters are more often in positions where they are blocked or allowed to do things by others.
Label: not_entailment
Knowledge: Common sense
Premise: Furthermore, male characters use inhibitory language more (inhib), which contains words pertaining to blocking or allowing, suggesting that these characters are more often in positions where they are blocked or allowed to do things by others.
Hypothesis: Furthermore, male characters use inhibitory language more (inhib), which contains words pertaining to blocking or allowing, suggesting that these characters are in positions of power.
Label: not_entailment
Knowledge: Common sense
Premise: Furthermore, male characters use inhibitory language more (inhib), which contains words pertaining to blocking or allowing, suggesting that these characters are in positions of power.
Hypothesis: Furthermore, male characters use inhibitory language more (inhib), which contains words pertaining to blocking or allowing, suggesting that these characters are in positions of low power.
Label: not_entailment
Predicate-argument structure: Intersectivity;Ellipsis/Implicits
Premise: Furthermore, male characters use inhibitory language more (inhib), which contains words pertaining to blocking or allowing, suggesting that these characters are in positions of low power.
Hypothesis: Furthermore, male characters use inhibitory language more (inhib), which contains words pertaining to blocking or allowing, suggesting that these characters are in positions of power.
Label: not_entailment
Predicate-argument structure: Intersectivity;Ellipsis/Implicits
Premise: Looking at pictures online of people trying to take photos of mirrors they want to sell is my new thing...
Hypothesis: Looking at pictures online of people trying to take photos of mirrors is my new thing...
Label: not_entailment
Logic: Non-monotone
Predicate-argument structure: Restrictivity
Premise: Looking at pictures online of people trying to take photos of mirrors is my new thing...
Hypothesis: Looking at pictures online of people trying to take photos of mirrors they want to sell is my new thing...
Label: not_entailment
Logic: Non-monotone
Predicate-argument structure: Restrictivity
Premise: A serene wind rolled across the glade.
Hypothesis: A tempestuous wind rolled across the glade.
Label: not_entailment
Lexical semantics: Lexical entailment
Premise: A tempestuous wind rolled across the glade.
Hypothesis: A serene wind rolled across the glade.
Label: not_entailment
Lexical semantics: Lexical entailment
Premise: A serene wind rolled across the glade.
Hypothesis: An easterly wind rolled across the glade.
Label: not_entailment
Lexical semantics: Lexical entailment
Premise: An easterly wind rolled across the glade.
Hypothesis: A serene wind rolled across the glade.
Label: not_entailment
Lexical semantics: Lexical entailment
Premise: A serene wind rolled across the glade.
Hypothesis: A calm wind rolled across the glade.
Label: entailment
Lexical semantics: Lexical entailment
Premise: A calm wind rolled across the glade.
Hypothesis: A serene wind rolled across the glade.
Label: entailment
Lexical semantics: Lexical entailment
Premise: A serene wind rolled across the glade.
Hypothesis: A wind rolled across the glade.
Label: entailment
Logic: Upward monotone
Predicate-argument structure: Intersectivity
Premise: A wind rolled across the glade.
Hypothesis: A serene wind rolled across the glade.
Label: not_entailment
Logic: Upward monotone
Predicate-argument structure: Intersectivity
Premise: The reaction was strongly exothermic.
Hypothesis: The reaction media got very hot.
Label: entailment
Knowledge: World knowledge
Premise: The reaction media got very hot.
Hypothesis: The reaction was strongly exothermic.
Label: entailment
Knowledge: World knowledge
Premise: The reaction was strongly exothermic.
Hypothesis: The reaction media got very cold.
Label: not_entailment
Knowledge: World knowledge
Premise: The reaction media got very cold.
Hypothesis: The reaction was strongly exothermic.
Label: not_entailment
Knowledge: World knowledge
Premise: The reaction was strongly endothermic.
Hypothesis: The reaction media got very hot.
Label: not_entailment
Knowledge: World knowledge
Premise: The reaction media got very hot.
Hypothesis: The reaction was strongly endothermic.
Label: not_entailment
Knowledge: World knowledge
Premise: The reaction was strongly endothermic.
Hypothesis: The reaction media got very cold.
Label: entailment
Knowledge: World knowledge
Premise: The reaction media got very cold.
Hypothesis: The reaction was strongly endothermic.
Label: entailment
Knowledge: World knowledge
Premise: She didn't think I had already finished it, but I had.
Hypothesis: I had already finished it.
Label: entailment
Predicate-argument structure: Ellipsis/Implicits
Premise: I had already finished it.
Hypothesis: She didn't think I had already finished it, but I had.
Label: not_entailment
Predicate-argument structure: Ellipsis/Implicits
Premise: She didn't think I had already finished it, but I had.
Hypothesis: I hadn't already finished it.
Label: not_entailment
Logic: Negation
Predicate-argument structure: Ellipsis/Implicits
Lexical semantics: Factivity
Premise: I hadn't already finished it.
Hypothesis: She didn't think I had already finished it, but I had.
Label: not_entailment
Logic: Negation
Predicate-argument structure: Ellipsis/Implicits
Lexical semantics: Factivity
Premise: She thought I had already finished it, but I hadn't.
Hypothesis: I had already finished it.
Label: not_entailment
Logic: Negation
Predicate-argument structure: Ellipsis/Implicits
Premise: I had already finished it.
Hypothesis: She thought I had already finished it, but I hadn't.
Label: not_entailment
Logic: Negation
Predicate-argument structure: Ellipsis/Implicits
Premise: She thought I had already finished it, but I hadn't.
Hypothesis: I hadn't already finished it.
Label: entailment
Predicate-argument structure: Ellipsis/Implicits
Lexical semantics: Factivity
Premise: I hadn't already finished it.
Hypothesis: She thought I had already finished it, but I hadn't.
Label: not_entailment
Predicate-argument structure: Ellipsis/Implicits
Lexical semantics: Factivity
Premise: Temple said that the business was facing difficulties, but didn't make any specific claims.
Hypothesis: Temple didn't make any specific claims.
Label: entailment
Predicate-argument structure: Coordination scope
Premise: Temple didn't make any specific claims.
Hypothesis: Temple said that the business was facing difficulties, but didn't make any specific claims.
Label: not_entailment
Predicate-argument structure: Coordination scope
Premise: Temple said that the business was facing difficulties, but didn't make any specific claims.
Hypothesis: The business didn't make any specific claims.
Label: not_entailment
Predicate-argument structure: Coordination scope
Premise: The business didn't make any specific claims.
Hypothesis: Temple said that the business was facing difficulties, but didn't make any specific claims.
Label: not_entailment
Predicate-argument structure: Coordination scope
Premise: Temple said that the business was facing difficulties, but didn't have a chance of going into the red.
Hypothesis: Temple didn't have a chance of going into the red.
Label: not_entailment
Predicate-argument structure: Coordination scope
Premise: Temple didn't have a chance of going into the red.
Hypothesis: Temple said that the business was facing difficulties, but didn't have a chance of going into the red.
Label: not_entailment
Predicate-argument structure: Coordination scope
Premise: Temple said that the business was facing difficulties, but didn't have a chance of going into the red.
Hypothesis: Temple said the business didn't have a chance of going into the red.
Label: entailment
Predicate-argument structure: Coordination scope
Premise: Temple said the business didn't have a chance of going into the red.
Hypothesis: Temple said that the business was facing difficulties, but didn't have a chance of going into the red.
Label: not_entailment
Predicate-argument structure: Coordination scope
Premise: The profits of the businesses that focused on branding were still negative.
Hypothesis: The businesses that focused on branding still had negative profits.
Label: entailment
Predicate-argument structure: Relative clauses
Premise: The businesses that focused on branding still had negative profits.
Hypothesis: The profits of the businesses that focused on branding were still negative.
Label: entailment
Predicate-argument structure: Relative clauses
Premise: The profits of the business that was most successful were still negative.
Hypothesis: The profits that focused on branding were still negative.
Label: not_entailment
Predicate-argument structure: Relative clauses
Premise: The profits that focused on branding were still negative.
Hypothesis: The profits of the business that was most successful were still negative.
Label: not_entailment
Predicate-argument structure: Relative clauses
Premise: The profits of the businesses that were highest this quarter were still negative.
Hypothesis: The businesses that were highest this quarter still had negative profits.
Label: not_entailment
Predicate-argument structure: Relative clauses
Premise: The businesses that were highest this quarter still had negative profits.
Hypothesis: The profits of the businesses that were highest this quarter were still negative.
Label: not_entailment
Predicate-argument structure: Relative clauses
Premise: The profits of the businesses that were highest this quarter were still negative.
Hypothesis: For the businesses, the profits that were highest were still negative.
Label: entailment
Predicate-argument structure: Relative clauses
Premise: For the businesses, the profits that were highest were still negative.
Hypothesis: The profits of the businesses that were highest this quarter were still negative.
Label: entailment
Predicate-argument structure: Relative clauses
Premise: I baked him a cake.
Hypothesis: I baked him.
Label: not_entailment
Predicate-argument structure: Datives
Premise: I baked him.
Hypothesis: I baked him a cake.
Label: not_entailment
Predicate-argument structure: Datives
Premise: I baked him a cake.
Hypothesis: I baked a cake for him.
Label: entailment
Predicate-argument structure: Datives
Premise: I baked a cake for him.
Hypothesis: I baked him a cake.
Label: entailment
Predicate-argument structure: Datives
Premise: I gave him a note.
Hypothesis: I gave a note to him.
Label: entailment
Predicate-argument structure: Datives
Premise: I gave a note to him.
Hypothesis: I gave him a note.
Label: entailment
Predicate-argument structure: Datives
Premise: Jake broke the vase.
Hypothesis: The vase broke.
Label: entailment
Predicate-argument structure: Core args
Premise: The vase broke.
Hypothesis: Jake broke the vase.
Label: not_entailment
Predicate-argument structure: Core args
Premise: Jake broke the vase.
Hypothesis: Jake broke.
Label: not_entailment
Predicate-argument structure: Core args
Premise: Jake broke.
Hypothesis: Jake broke the vase.
Label: not_entailment
Predicate-argument structure: Core args
Premise: He is someone of many talents.
Hypothesis: He has many talents.
Label: entailment
Predicate-argument structure: Genitives/Partitives
Premise: He has many talents.
Hypothesis: He is someone of many talents.
Label: entailment
Predicate-argument structure: Genitives/Partitives
Premise: He is someone of many talents.
Hypothesis: His talents are many.
Label: entailment
Predicate-argument structure: Genitives/Partitives
Premise: His talents are many.
Hypothesis: He is someone of many talents.
Label: entailment
Predicate-argument structure: Genitives/Partitives
Premise: I don't want to have to keep fighting pesky bedbugs.
Hypothesis: I don't want to have to keep fighting bedbugs—they are so pesky.
Label: entailment
Predicate-argument structure: Restrictivity;Anaphora/Coreference
Premise: I don't want to have to keep fighting bedbugs—they are so pesky.
Hypothesis: I don't want to have to keep fighting pesky bedbugs.
Label: entailment
Predicate-argument structure: Restrictivity;Anaphora/Coreference
Premise: I don't want to have to keep entertaining people who don't value my time.
Hypothesis: I don't want to have to keep entertaining people.
Label: not_entailment
Logic: Downward monotone
Predicate-argument structure: Restrictivity;Relative clauses
Premise: I don't want to have to keep entertaining people.
Hypothesis: I don't want to have to keep entertaining people who don't value my time.
Label: entailment
Logic: Downward monotone
Predicate-argument structure: Restrictivity;Relative clauses
Premise: It's not the case that there is no rabbi at this wedding; he is right there standing behind that tree.
Hypothesis: There is a rabbi at this wedding; he is right there standing behind that tree.
Label: entailment
Logic: Double negation
Premise: There is a rabbi at this wedding; he is right there standing behind that tree.
Hypothesis: It's not the case that there is no rabbi at this wedding; he is right there standing behind that tree.
Label: entailment
Logic: Double negation
Premise: It's not the case that there is no rabbi at this wedding; he is right there standing behind that tree.
Hypothesis: There is no rabbi at this wedding, let alone behind that tree.
Label: not_entailment
Logic: Double negation;Negation
Premise: There is no rabbi at this wedding, let alone behind that tree.
Hypothesis: It's not the case that there is no rabbi at this wedding; he is right there standing behind that tree.
Label: not_entailment
Logic: Double negation;Negation
Premise: It's not the case that there is no rabbi at this wedding; he is right there standing behind that tree.
Hypothesis: A rabbi is at this wedding, standing right there standing behind that tree.
Label: entailment
Logic: Double negation
Predicate-argument structure: Anaphora/Coreference
Premise: A rabbi is at this wedding, standing right there standing behind that tree.
Hypothesis: It's not the case that there is no rabbi at this wedding; he is right there standing behind that tree.
Label: entailment
Logic: Double negation
Predicate-argument structure: Anaphora/Coreference
Premise: There is a rabbi at this wedding; he is right there standing behind that tree.
Hypothesis: A rabbi is at this wedding, standing right there standing behind that tree.
Label: entailment
Predicate-argument structure: Anaphora/Coreference
Premise: A rabbi is at this wedding, standing right there standing behind that tree.
Hypothesis: There is a rabbi at this wedding; he is right there standing behind that tree.
Label: entailment
Predicate-argument structure: Anaphora/Coreference
Premise: The first pleasurable experience I had interacting with another human being was at the age of seventeen.
Hypothesis: The first experience I had interacting with another human being was at the age of seventeen.
Label: not_entailment
Logic: Non-monotone
Predicate-argument structure: Intersectivity
Premise: The first experience I had interacting with another human being was at the age of seventeen.
Hypothesis: The first pleasurable experience I had interacting with another human being was at the age of seventeen.
Label: not_entailment
Logic: Non-monotone
Predicate-argument structure: Intersectivity
Premise: The first pleasurable experience I had interacting with another human being was at the age of seventeen.
Hypothesis: The first displeasurable experience I had interacting with another human being was at the age of seventeen.
Label: not_entailment
Logic: Non-monotone
Predicate-argument structure: Intersectivity
Premise: The first displeasurable experience I had interacting with another human being was at the age of seventeen.
Hypothesis: The first pleasurable experience I had interacting with another human being was at the age of seventeen.
Label: not_entailment
Logic: Non-monotone
Predicate-argument structure: Intersectivity
Premise: It being his first night in the US, he eagerly got a drink at the bar after showing his ID.
Hypothesis: He is over 20 years of age.
Label: entailment
Logic: Intervals/Numbers
Knowledge: World knowledge
Premise: He is over 20 years of age.
Hypothesis: It being his first night in the US, he eagerly got a drink at the bar after showing his ID.
Label: not_entailment
Logic: Intervals/Numbers
Knowledge: World knowledge
Premise: It being his first night in the US, he eagerly got a drink at the bar after showing his ID.
Hypothesis: He is under 21 years of age.
Label: not_entailment
Logic: Intervals/Numbers
Knowledge: World knowledge
Premise: He is under 21 years of age.
Hypothesis: It being his first night in the US, he eagerly got a drink at the bar after showing his ID.
Label: not_entailment
Logic: Intervals/Numbers
Knowledge: World knowledge
Premise: It being his first night in the US, he eagerly got a drink at the bar after showing his ID.
Hypothesis: He has a beard.
Label: not_entailment
Knowledge: World knowledge
Premise: He has a beard.
Hypothesis: It being his first night in the US, he eagerly got a drink at the bar after showing his ID.
Label: not_entailment
Knowledge: World knowledge
Premise: I can make scrambled eggs, but not complex dishes like consommé.
Hypothesis: Consommé is more complex to make than scrambled eggs.
Label: entailment
Knowledge: Common sense
Premise: Consommé is more complex to make than scrambled eggs.
Hypothesis: I can make scrambled eggs, but not complex dishes like consommé.
Label: not_entailment
Knowledge: Common sense
Premise: I can make scrambled eggs, but not complex dishes like consommé.
Hypothesis: Consommé is not more complex to make than scrambled eggs.
Label: not_entailment
Logic: Negation
Knowledge: Common sense