CO - NNECT : A Framework for Revealing Commonsense Knowledge Paths as Explicitations of Implicit Knowledge in Texts (2021)

Abstract: In this work we leverage commonsense knowledge in form of knowledge paths to establish connections between sentences, as a form of explicitation of implicit knowledge. Such connections can be direct (singlehop paths) or require intermediate concepts (multihop paths). To construct such paths we combine two model types in a joint framework we call Co-nnect: a relation classifier that predicts direct connections between concepts; and a target prediction model that generates target or intermediate concepts given a source concept and a relation, which we use to construct multihop paths. Unlike prior work that relies exclusively on static knowledge sources, we leverage language models finetuned on knowledge stored in ConceptNet, to dynamically generate knowledge paths, as explanations of implicit knowledge that connects sentences in texts. As a central contribution we design manual and automatic evaluation settings for assessing the quality of the generated paths. We conduct evaluations on two argumentative datasets and show that a combination of the two model types generates meaningful, high-quality knowledge paths between sentences that reveal implicit knowledge conveyed in text.

## introduction commonsense knowledge covers simple facts about the world, people and everyday life, e.g., birds can fly or cars are used for driving. it is increasily used for many nlp tasks, e.g. for question answering @xcite , textual entailment @xcite , or classifying argumentative functions @xcite . in this work, we leverage commonsense knowledge in the form of single-and multihop knowledge paths for establishing connections between concepts from different sentences in texts, and show that these paths can explicate implicit information conveyed by the text. connections can either be direct, e.g. given the sentences the car was too old and the engine broke down, the concepts car and engine can be linked with a direct relation (singlehop path) @xmath0; or indirect -here intermediate concepts are required to establish the link, as between berliners produce too much waste and environmental protection should play a more important role, where the link between waste and environmental protection requires a multihop reasoning path: @xmath1. we show that two complementary model types can be combined to solve the two subtasks: (i) for predicting singlehop paths between concepts, we propose a relation classification model that is very precise, but restricted to direct connections between concepts; (ii) for constructing longer paths we rely on a target prediction model that can generate intermediate concepts and is thus able to generate multihop paths. however, the intermediate concepts can be irrelevant or misleading. to our knowledge, prior work has applied either relation classification or target prediction models. we propose co-nnect, a framework that establishes commonsense knowledge paths for con-necting sentences by combining relation classification and target prediction models, leveraging their strengths and minimizing their weaknesses. with co-nnect, we obtain high-quality knowledge paths that explicate implicit knowledge conveyed by the text. we focus on commonsense knowledge in con-ceptnet @xcite , a knowledge graph (kg) that represents concepts (words or phrases) as nodes, and relations between them as edges, e.g., oven,usedfor, baking . as instances of the model types we use corec @xcite , a multi-label relation classifier that predicts relation types and that we enhance with a pretrained language model; and comet @xcite , a pretrained transformer model that learns to generate target concepts given a source concept and a relation. in contrast to models that retrieve knowledge from static kgs @xcite , both models are fine-tuned on con-ceptnet and applied on the fly, to dynamically generate knowledge paths that generalize beyond the static knowledge, allowing us to predict unseen knowledge paths. we compare our models to a baseline model that solely relies on static kgs. we evaluate our framework on two english argumentative datasets, ikat @xcite and arc @xcite , which offer annotations that explain implicit connections between sentences. while knowledge paths have been widely used in nlp downstream tasks, a careful evaluation of these paths has not received much attention. as a central contribution of our work, we address this shortcoming by designing manual and automatic settings for path evaluation: we evaluate the relevance and quality of the paths and their ability to represent implicit knowledge in an annotation experiment; and we compare the paths to the annotations of implicit knowledge in ikat and arc, using automatic similarity metrics. our main contributions are: i) we propose co-nnect, a framework that combines two complementary types of knowledge path prediction models that have previously only been applied separately; foot_0 ii) we show that commonsense knowledge paths generated with co-nnect effectively represent implied knowledge between sentences; iii) we propose an evaluation scheme that measures the quality of the knowledge paths, going beyond many approaches that use knowledge paths for downstream applications without analyzing their quality. ## related work in this work we combine relation classification and target prediction for generating commonsense knowledge representations over text. relation classification covers a range of methods and learning paradigms for representing relations. a variety of neural architectures such as rnns @xcite , cnns @xcite , sequenceto-sequence models @xcite or language models @xcite achieved state-of-the-art results. to our knowledge, @xcite is the only work that proposed a relation classification model specifically for conceptnet relations, which we adapt for our work. besides comet @xcite , the model used in our approach, @xcite perform target prediction on conceptnet using an attentional encoder-decoder model. they improve the kb completion model of @xcite by jointly scoring triples and predicting target concepts. ## enriching texts with commonsense knowledge paths this section describes co-nnect, the framework we propose for enriching texts with commonsense knowledge, by establishing relations or paths between concepts from different sentences. towards this aim, we apply relation classification and target prediction models in combination. we first characterize differences between the two model types and their instantiations, corec-lm and comet, describe how we adapt them to our task and evaluate them on conceptnet to assess their performance ( §3.1). we then show how we utilize the models to establish connections between concepts in texts ( §3.2) and present a baseline model that uses con-ceptnet as a static kg to establish commonsense knowledge paths ( §3.3). ## revealing implicit knowledge through knowledge paths: experiments and evaluation in this section we evaluate the paths generated by our proposed models. we first present our datasets and statistics on established connections ( §4.1), and then evaluate the quality of the paths manually ( §4.2) and automatically ( §4.3). ## conclusion our work aims to leverage commonsense knowledge in the form of single and multihop paths, to establish knowledge connections between concepts from different sentences, as a form of explicitation of implicitly conveyed information. we combine existing relation classification and target prediction models in a dynamic knowledge prediction framework, co-nnect, utilizing language models finetuned on knowledge relations from concept-net. we compare against a path ranking system that employs static knowledge from conceptnet as a baseline and evaluate the quality of the obtained paths (i) through manual evaluation and (ii) using automatic similarity metrics, by comparing generated paths to annotations of implicit knowledge in two argumentative datasets. our evaluations show that we obtain the highest number of connections from the static conceptnet graph, however, they are often noisy due to unrelated intermediate nodes, and -even after replacements -still contain many unspecific relations. our framework co-nnect, instead, combines relation classification and target prediction, leveraging the high precision of the former, and the ability to perform forward chaining of the latter, and obtain high-quality, meaningful and relevant knowledge paths that reveal implicit knowledge conveyed by the text, as shown in a profound manual evaluation experiment. we believe that co-nnect is a useful framework which can be applied for different tasks, such as enriching texts with commonsense knowledge relations and paths, for dynamically enriching knowledge bases, or for building knowledge constraints for language generation. in @xcite for example we inject single-and multihop commonsense knowledge paths predicted by co-nnect as constraints into language models and show that this improves the model's ability of generating sentences that explicate implicit knowledge which connects sentences in texts. we furthermore believe that the paths established with co-nnect, which can provide explicitations of implicit knowledge, can be useful to enhance many other nlp downstream tasks, such as argument classification, stance detection, or commonsense reasoning.