Do you want to publish a course? Click here

Modeling Event Plausibility with Consistent Conceptual Abstraction

نمذجة معقولية الحدث مع التجريد المفاهيمي المتسق

258   0   0   0.0 ( 0 )
 Publication date 2021
and research's language is English
 Created by Shamra Editor




Ask ChatGPT about the research

Understanding natural language requires common sense, one aspect of which is the ability to discern the plausibility of events. While distributional models---most recently pre-trained, Transformer language models---have demonstrated improvements in modeling event plausibility, their performance still falls short of humans'. In this work, we show that Transformer-based plausibility models are markedly inconsistent across the conceptual classes of a lexical hierarchy, inferring that a person breathing'' is plausible while a dentist breathing'' is not, for example. We find this inconsistency persists even when models are softly injected with lexical knowledge, and we present a simple post-hoc method of forcing model consistency that improves correlation with human plausibility judgements.



References used
https://aclanthology.org/
rate research

Read More

We present a system for learning generalized, stereotypical patterns of events---or schemas''---from natural language stories, and applying them to make predictions about other stories. Our schemas are represented with Episodic Logic, a logical form that closely mirrors natural language. By beginning with a head start'' set of protoschemas--- schemas that a 1- or 2-year-old child would likely know---we can obtain useful, general world knowledge with very few story examples---often only one or two. Learned schemas can be combined into more complex, composite schemas, and used to make predictions in other stories where only partial information is available.
Storytelling, whether via fables, news reports, documentaries, or memoirs, can be thought of as the communication of interesting and related events that, taken together, form a concrete process. It is desirable to extract the event chains that repres ent such processes. However, this extraction remains a challenging problem. We posit that this is due to the nature of the texts from which chains are discovered. Natural language text interleaves a narrative of concrete, salient events with background information, contextualization, opinion, and other elements that are important for a variety of necessary discourse and pragmatics acts but are not part of the principal chain of events being communicated. We introduce methods for extracting this principal chain from natural language text, by filtering away non-salient events and supportive sentences. We demonstrate the effectiveness of our methods at isolating critical event chains by comparing their effect on downstream tasks. We show that by pre-training large language models on our extracted chains, we obtain improvements in two tasks that benefit from a clear understanding of event chains: narrative prediction and event-based temporal question answering. The demonstrated improvements and ablative studies confirm that our extraction method isolates critical event chains.
Prior research has explored the ability of computational models to predict a word semantic fit with a given predicate. While much work has been devoted to modeling the typicality relation between verbs and arguments in isolation, in this paper we tak e a broader perspective by assessing whether and to what extent computational approaches have access to the information about the typicality of entire events and situations described in language (Generalized Event Knowledge). Given the recent success of Transformers Language Models (TLMs), we decided to test them on a benchmark for the dynamic estimation of thematic fit. The evaluation of these models was performed in comparison with SDM, a framework specifically designed to integrate events in sentence meaning representations, and we conducted a detailed error analysis to investigate which factors affect their behavior. Our results show that TLMs can reach performances that are comparable to those achieved by SDM. However, additional analysis consistently suggests that TLMs do not capture important aspects of event knowledge, and their predictions often depend on surface linguistic features, such as frequent words, collocations and syntactic patterns, thereby showing sub-optimal generalization abilities.
Universal Conceptual Cognitive Annotation (UCCA) is a semantic annotation scheme that organizes texts into coarse predicate-argument structure, offering broad coverage of semantic phenomena. At the same time, there is still need for a finer-grained t reatment of many of the categories. The Adverbial category is of special interest, as it covers a wide range of fundamentally different meanings such as negation, causation, aspect, and event quantification. In this paper we introduce a refinement annotation scheme for UCCA's Adverbial category, showing that UCCA Adverbials can indeed be subcategorized into at least 7 semantic types, and doing so can help clarify and disambiguate the otherwise coarse-grained labels. We provide a preliminary set of annotation guidelines, as well as pilot annotation experiments with high inter-annotator agreement, confirming the validity of the scheme.
We study the problem of Event Causality Identification (ECI) to detect causal relation between event mention pairs in text. Although deep learning models have recently shown state-of-the-art performance for ECI, they are limited to the intra-sentence setting where event mention pairs are presented in the same sentences. This work addresses this issue by developing a novel deep learning model for document-level ECI (DECI) to accept inter-sentence event mention pairs. As such, we propose a graph-based model that constructs interaction graphs to capture relevant connections between important objects for DECI in input documents. Such interaction graphs are then consumed by graph convolutional networks to learn document context-augmented representations for causality prediction between events. Various information sources are introduced to enrich the interaction graphs for DECI, featuring discourse, syntax, and semantic information. Our extensive experiments show that the proposed model achieves state-of-the-art performance on two benchmark datasets.

suggested questions

comments
Fetching comments Fetching comments
Sign in to be able to follow your search criteria
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا