Следование

This paper advocates a new architecture for tex- tual inference in which finding a good alignment is separated from evaluating entailment. Current ap- proaches to semantic inference in question answer- …...

We introduce two ways to detect entail- ment using distributional semantic repre- sentations of phrases. Our first experiment shows that the entailment relation between adjective-noun constructions and their head nouns …...

This paper reports the description and scores of our system, FBK-TR, which participated at the SemEval 2014 task #1 "Evaluation of Compositional Distribu- tional Semantic Models on Full Sentences through …...

This paper presents the task on the evalu- ation of Compositional Distributional Se- mantics Models on full sentences orga- nized for the first time within SemEval- 2014. Participation was open …...

Understanding entailment and contradic- tion is fundamental to understanding nat- ural language, and inference about entail- ment and contradiction is a valuable test- ing ground for the development of seman- …...

Distributional representations of words have been recently used in supervised settings for recognizing lexical inference relations be- tween word pairs, such as hypernymy and en- tailment. We investigate a collection …...

AbstractTransfer and multi-task learning have traditionally focused on either a single source-target pair or very few, similar tasks. Ideally, the linguistic levels of morphology, syntax and semantics would benefit each …...

AbstractWord embeddings provide point representations of words containing useful semantic information. We introduce multimodal word distributions formed from Gaussian mixtures, for multiple word meanings, entailment, and rich uncertainty information. To …...

AbstractModeling hypernymy, such as poodle is-a dog, is an important generalization aid to many NLP tasks, such as entailment, relation extraction, and question answering. Supervised learning from labeled hypernym sources, …...

AbstractSentence simplification aims to improve readability and understandability, based on several operations such as splitting, deletion, and paraphrasing. However, a valid simplified sentence should also be logically entailed by its …...

AbstractNeural activation models have been proposed in the literature that use a set of example words for which fMRI measurements are available in order to find a mapping between word …...

AbstractThis work examines the robustness of self-attentive neural networks against adversarial input perturbations. Specifically, we investigate the attention and feature extraction mechanisms of state-of-the-art recurrent neural networks and self-attentive architectures …...

AbstractQuestion Answering (QA) naturally reduces to an entailment problem, namely, verifying whether some text entails the answer to a question. However, for multi-hop QA tasks, which require reasoning with multiple …...

AbstractWe present an unsupervised method to generate Word2Sense word embeddings that are interpretable — each dimension of the embedding space corresponds to a fine-grained sense, and the non-negative value of …...

AbstractWe investigate the extent to which pre-trained language models acquire analytical and deductive logical reasoning capabilities as a side effect of learning word prediction. We present AnaLog, a natural language …...

AbstractNatural language inference (NLI) has been widely used as a task to train and evaluate models for language understanding. However, the ability of NLI models to perform inferences requiring understanding …...

AbstractEntailment has been recognized as an important metric for evaluating natural language understanding (NLU) models, and recent studies have found that entailment pretraining benefits weakly supervised fine-tuning. In this work, …...

AbstractWe present a system for natural language inference in breast cancer clinical trial reports, as framed by SemEval 2023 Task 7: Multi-evidence Natural Language Inference for Clinical Trial Data. In …...

AbstractIn this paper, we focus on the ability of large language models (LLMs) to accommodate different pragmatic sentence types, such as questions, commands, as well as sentence fragments for natural …...