Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userWeisfeiler-Leman in the Bamboo: Novel AMR Graph Metrics and a Benchmark for AMR Graph Similarity
Weisveileer-Leman في الخيزران: رواية AMR الرسم البياني المقاييس معيار لتشابه AMR الرسم البياني
500
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
مجردة، تم اقتراح العديد من المقاييس لتقييم تشابه (مجردة) بمعنى تمثيلات (AMRS)، لكن القليل يعرف عن الطريقة التي تتعلق بتصنيفات التشابه البشري. علاوة على ذلك، فإن المقاييس الحالية لديها نقاط القوة والضعف التكميلية: يتأكيد البعض على السرعة، في حين أن البعض الآخر يجعل محاذاة هياكل الرسوم البيانية الصريحة، بسعر خطوة محاذاة مكلفة. في هذا العمل، نقترح مقاييس تشابه Weisfeiler-Leman Amr الجديدة التي توحد نقاط القوة المقاييس السابقة، مع تخفيف نقاط ضعفها. على وجه التحديد، فإن مقاييسنا الجديدة قادرة على مطابقة التحسسات الفاصلة والحرية والحث على N: M بين العقد. علاوة على ذلك، نقدم معيارا لمقاييس AMR بناء على الأهداف العلنية (الخيزران)، أول معيار لدعم التقييم التجريبي لمقاييس التشابه الرسمي في الرسم البياني. يزيد الخيزران إمكانية تفسير النتائج عن طريق تحديد أهداف علنية متعددة تتراوح بين أهداف تشابه الجملة لإجراء اختبارات الإجهاد التي تحقق متانة متري ضد تحويلات الرسم البياني المعني بالمعنى والمعنى. نعرض فوائد الخيزران عن طريق تنميط المقاييس السابقة ومقاييس خاصة بنا. تشير النتائج إلى أن مقاييس جديدة قد تكون بمثابة خط أساس قوي للعمل في المستقبل.
Abstract Several metrics have been proposed for assessing the similarity of (abstract) meaning representations (AMRs), but little is known about how they relate to human similarity ratings. Moreover, the current metrics have complementary strengths and weaknesses: Some emphasize speed, while others make the alignment of graph structures explicit, at the price of a costly alignment step. In this work we propose new Weisfeiler-Leman AMR similarity metrics that unify the strengths of previous metrics, while mitigating their weaknesses. Specifically, our new metrics are able to match contextualized substructures and induce n:m alignments between their nodes. Furthermore, we introduce a Benchmark for AMR Metrics based on Overt Objectives (Bamboo), the first benchmark to support empirical assessment of graph-based MR similarity metrics. Bamboo maximizes the interpretability of results by defining multiple overt objectives that range from sentence similarity objectives to stress tests that probe a metric's robustness against meaning-altering and meaning- preserving graph transformations. We show the benefits of Bamboo by profiling previous metrics and our own metrics. Results indicate that our novel metrics may serve as a strong baseline for future work.
References used
https://aclanthology.org/
rate research
Read More
AMR (Abstract Meaning Representation) and EDS (Elementary Dependency Structures) are two popular meaning representations in NLP/NLU. AMR is more abstract and conceptual, while EDS is more low level, closer to the lexical structures of the given sente
nces. It is thus not surprising that EDS parsing is easier than AMR parsing. In this work, we consider using information from EDS parsing to help improve the performance of AMR parsing. We adopt a transition-based parser and propose to add EDS graphs as additional semantic features using a graph encoder composed of LSTM layer and GCN layer. Our experimental results show that the additional information from EDS parsing indeed gives a boost to the performance of the base AMR parser used in our experiments.
Coupled with biaffine decoders, transformers have been effectively adapted to text-to-graph transduction and achieved state-of-the-art performance on AMR parsing. Many prior works, however, rely on the biaffine decoder for either or both arc and labe
l predictions although most features used by the decoder may be learned by the transformer already. This paper presents a novel approach to AMR parsing by combining heterogeneous data (tokens, concepts, labels) as one input to a transformer to learn attention, and use only attention matrices from the transformer to predict all elements in AMR graphs (concepts, arcs, labels). Although our models use significantly fewer parameters than the previous state-of-the-art graph parser, they show similar or better accuracy on AMR 2.0 and 3.0.
The next generation of conversational AI systems need to: (1) process language incrementally, token-by-token to be more responsive and enable handling of conversational phenomena such as pauses, restarts and self-corrections; (2) reason incrementally
allowing meaning to be established beyond what is said; (3) be transparent and controllable, allowing designers as well as the system itself to easily establish reasons for particular behaviour and tailor to particular user groups, or domains. In this short paper we present ongoing preliminary work combining Dynamic Syntax (DS) - an incremental, semantic grammar framework - with the Resource Description Framework (RDF). This paves the way for the creation of incremental semantic parsers that progressively output semantic RDF graphs as an utterance unfolds in real-time. We also outline how the parser can be integrated with an incremental reasoning engine through RDF. We argue that this DS-RDF hybrid satisfies the desiderata listed above, yielding semantic infrastructure that can be used to build responsive, real-time, interpretable Conversational AI that can be rapidly customised for specific user groups such as people with dementia.
A mind-map is a diagram that represents the central concept and key ideas in a hierarchical way. Converting plain text into a mind-map will reveal its key semantic structure and be easier to understand. Given a document, the existing automatic mind-m
ap generation method extracts the relationships of every sentence pair to generate the directed semantic graph for this document. The computation complexity increases exponentially with the length of the document. Moreover, it is difficult to capture the overall semantics. To deal with the above challenges, we propose an efficient mind-map generation network that converts a document into a graph via sequence-to-graph. To guarantee a meaningful mind-map, we design a graph refinement module to adjust the relation graph in a reinforcement learning manner. Extensive experimental results demonstrate that the proposed approach is more effective and efficient than the existing methods. The inference time is reduced by thousands of times compared with the existing methods. The case studies verify that the generated mind-maps better reveal the underlying semantic structures of the document.
Precisely defining the terminology is the first step in scientific communication. Developing neural text generation models for definition generation can circumvent the labor-intensity curation, further accelerating scientific discovery. Unfortunately
, the lack of large-scale terminology definition dataset hinders the process toward definition generation. In this paper, we present a large-scale terminology definition dataset Graphine covering 2,010,648 terminology definition pairs, spanning 227 biomedical subdisciplines. Terminologies in each subdiscipline further form a directed acyclic graph, opening up new avenues for developing graph-aware text generation models. We then proposed a novel graph-aware definition generation model Graphex that integrates transformer with graph neural network. Our model outperforms existing text generation models by exploiting the graph structure of terminologies. We further demonstrated how Graphine can be used to evaluate pretrained language models, compare graph representation learning methods and predict sentence granularity. We envision Graphine to be a unique resource for definition generation and many other NLP tasks in biomedicine.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
