It’s Commonsense, isn’t it? Demystifying Human Evaluations in Commonsense-Enhanced NLG Systems (2021)

Abstract: Common sense is an integral part of human cognition which allows us to make sound decisions, communicate effectively with others and interpret situations and utterances. Endowing AI systems with commonsense knowledge capabilities will help us get closer to creating systems that exhibit human intelligence. Recent efforts in Natural Language Generation (NLG) have focused on incorporating commonsense knowledge through large-scale pre-trained language models or by incorporating external knowledge bases. Such systems exhibit reasoning capabilities without common sense being explicitly encoded in the training set. These systems require careful evaluation, as they incorporate additional resources during training which adds additional sources of errors. Additionally, human evaluation of such systems can have significant variation, making it impossible to compare different systems and define baselines. This paper aims to demystify human evaluations of commonsense-enhanced NLG systems by proposing the Commonsense Evaluation Card (CEC), a set of recommendations for evaluation reporting of commonsense-enhanced NLG systems, underpinned by an extensive analysis of human evaluations reported in the recent literature.

## introduction commonsense knowledge is vital for human communication, as it helps us make inferences without explicitly mentioning the context. recently, there has been an interest in developing natural language generation (nlg) systems that exhibit commonsense abilities (e.g. @xcite ). although everyone understands what common sense is, defining it remains a challenge as it is highly context-dependent. common sense can be defined as "simple wisdom" (oxford english dictionary * * equal contribution online), "the ability to use good judgment in making decisions and to live in a reasonable and safe way" (cambridge dictionary), or as a "sound and prudent judgment based on a simple perception of the situation or facts" (mirriam webster). common sense involves language understanding and reasoning abilities, representing a key factor for establishing effective interactions between humans and machines @xcite . in his pioneering work, @xcite proposes that "a program has common sense if it automatically deduces for itself a sufficiently wide class of immediate consequences of anything it is told and what it already knows". traditionally, commonsense knowledge has been injected in nlg systems either implicitly in the form of rules and/or explicitly with semantic representations in the form of external knowledge bases or ontologies. for instance, expert domain nlg systems (such as the babytalk system @xcite ) have incorporated external knowledge in the form of a clinical ontology. in these expert domain nlg systems, knowledge (which might include procedural knowledge) is represented in rules that are built into the system and have been acquired through experts via interviews, observations or other approaches @xcite . most recent challenges have focused on injecting commonsense knowledge into neural nlg models in two ways: through pre-trained models and through utilising commonsense graphs or knowledge bases. the former assumes that pre-trained models already contain commonsense knowledge @xcite . the latter incorporate entity relationships derived from semantic graphs (e.g. concept-net @xcite ) or knowledge bases (e.g. @xcite ). it is clear that the incorporation of external knowledge of some form has always been at the heart of nlg system development. in this paper, we are interested in examining how commonsenseenhanced nlg systems are evaluated and whether the accuracy of the underlying commonsense knowledge is assessed by the system creators. to our knowledge, there are no automatic metrics available for commonsense evaluation, and therefore we focus only on human evaluations. human evaluation is an area that has received an increasing amount of scrutiny within the wider nlg research community. previous work has highlighted issues with regards to missing details in evaluations, lack of proper analysis of results obtained, variability in the use of names and definitions of evaluated aspects of output quality (van der @xcite and a mismatch on evaluation methods chosen which is correlated with the publication venue rather than the nlg task @xcite . after examining the last twenty years of human evaluations in nlg, recent survey work has found systemic issues with high levels of diversity of evaluation approaches, inconsistencies and variability in quality criterion names, missing definitions, and fundamental reporting gaps @xcite . these issues mean there is a pressing need to better understand the state of human evaluations in other niche areas of nlg such as those systems enhanced with commonsense knowledge. the contributions of this paper are three-fold: (1) we firstly present an annotated dataset of papers reporting commonsense-enhanced nlg systems published between 2018-2020 in acl conferences; (2) we present a detailed analysis on human evaluation including reporting on what criteria researchers have most commonly used and whether they have evaluated the underlying commonsense knowledge on its own right and through the generated text; and the incorporation of commonsense knowledge is considered a challenging task within ai. this challenge is due to the fact that commonsense reasoning or knowledge is considered a black box, as there is uncertainty on how to represent knowledge in order to solve commonsense reasoning problems @xcite . the reliance on existing knowledge bases to incorporate this type of broadbased knowledge might not be sufficient as it may, in many cases, fail to incorporate explicit fundamental knowledge @xcite . pre-trained models, on the other hand, have capabilities of learning relational patterns and can achieve commonsense reasoning without explicit knowledge representation, as conveyed in the traditional pipelines @xcite . however, it remains unclear how the reasoning is performed and how prior knowledge is learned in the training phase @xcite . ## paper selection & annotation we used the prisma method @xcite to select papers to be included in this study following @xcite and @xcite . we began by considering all papers published in acl venues (acl, cl, conll, emnlp, findings, naacl, semeval, *sem, tacl and inlg) in the past three years (2018-2020). we screened the papers using the following search terms (in their title): commonsense, generation, reasoning, domain knowledge, expert, expertise, sensible, ontology, knowledge. this left us with 129 papers. from these, we randomly pick 55 papers that were annotated by the authors of this paper, following the annotation scheme proposed by @xcite . papers on commonsense reasoning can either focus on language generation or understanding. for instance, commonsense reasoning can be ad-dressed as a classification task, where based on the context, a reasoning system can choose an option from a set of options @xcite . during annotation, such papers were omitted. following @xcite , papers were annotated using the three broad categories: (1) system attributes (input, output, task and language) which describe evaluated nlg systems, (2) quality criterion attributes (verbatim criterion name, definition and paraphrase), and (3) operationalisation attributes (e.g. type of instruments, type of collected data etc.) which specify how evaluations are performed. in addition to these, we introduced a fourth category, commonsense knowledge, with five new annotation items which are relevant for commonsense-enhanced nlg, namely: these additional items were deemed important to investigate whether there is a relationship between the human evaluation criteria and the type of commonsense knowledge covered by the nlg system. in addition, when evaluating generated text, it is vital to know whether errors in the generated text arise from the underlying data or the text generator. ## analysis and results in this section, we present the results from the analysis of the annotated papers. the annotations and the developed code can be found in the projects' repository foot_0 . verbatim criterion name count fluency 6 coherence 4 informativeness 3 grammaticality, correctness, diversity, appropriateness, accuracy 2 commonsense, topic-consistency, sarcasticness, interpretability, engagement, commonsense plausibility, commonsense reasoning, reasonability, novelty, usefulness, intention, information, naturalness, logicality, humour, relevance, common ground, answerability, plausible, effect, validity, quality, eventcentered commonsense reasoning, bestworst scaling, consistency, attribute, creativity, effectiveness 1 mixed: grammatical correctness and fluency 2 none given 3 table the 34 papers in the dataset corresponded to 70 individual evaluations, amounting to 2.05 evaluations per paper. this dataset was annotated between three annotators taking approximately 20 minutes or more to annotate each paper. in the following subsections we will first report the paper and system level statistics (section 4.1), followed by evaluation-level statistics for the quality-criterion (section 4.2), then the operationalisation attributes (section 4.3), and finally the commonsense criteria findings (section 4.4). ## the commonsense evaluation card the commonsense evaluation card (cec) ( cec includes three main sections: (1) definition of common sense in the context of the reported work and the type of commonsense knowledge; (2) evaluation of the validity of external commonsense knowledge; and (3) evaluation of commonsense knowledge in a generated text. ## conclusions this paper presented a human evaluation analysis on works describing systems that incorporate commonsense knowledge or other external knowledge bases with the aim to enhance the reasoning abilities of nlg systems. we have utilised an annotation scheme that has been verified in previous work and we have enhanced it with five additional criteria relevant for commonsense-enhanced nlg systems and we have reported our analysis of the annotations. our analysis showed that there is a large variability on how such systems are evaluated, the type of evaluation criteria that are selected and we questioned whether standard nlg criteria are fit for purpose when evaluating reasoning abilities. we have therefore recommended that researchers should evaluate the reasoning ability of their systems (in addition to standard nlg metrics). we did not specify how these evaluations should be performed as this can vary depending on the task. we recommend nevertheless, that authors provide their definition(s) of commonsense knowledge to their evaluators. additionally, we recommend that researchers validate their external knowledge bases to ensure that any errors present in generated output are not derived from the underlying knowledge. finally, as this field grows in the future and attracts further attention, it would be useful to document commonsense knowledge errors in a more structured way, as for instance in @xcite .