F act S potter: Evaluating the Factual Faithfulness of Graph-to-Text Generation (2023)

Abstract: Graph-to-text (G2T) generation takes a graph as input and aims to generate a fluent and faith- ful textual representation of the information in the graph. The task has many applications, such as dialogue generation and question an- swering. In this work, we investigate to what extent the G2T generation problem is solved for previously studied datasets, and how pro- posed metrics perform when comparing generated texts. To help address their limitations, we propose a new metric that correctly identifies factual faithfulness, i.e., given a triple (subject, predicate, object), it decides if the triple is present in a generated text. We show that our metric FactSpotter achieves the highest correlation with human annotations on data correct- ness, data coverage, and relevance. In addition, FactSpotter can be used as a plug-in feature to improve the factual faithfulness of existing models. Finally, we investigate if existing G2T datasets are still challenging for state-of-the-art models. Our code is available online: https://github.com/guihuzhang/FactSpotter.

## introduction graph-to-text (g2t) generation is an important task in natural language generation, as it renders graphs, and in particular knowledge graphs, accessible to non-technical users in downstream applications such as question answering @xcite , @xcite , knowledge-grounded dialogue generation @xcite , and document summarization @xcite . in recent years, there have been several datasets @xcite and methods proposed for g2t generation @xcite , in addition to g2t competitions @xcite . evaluating text generation is a challenging task in itself @xcite ; moreover, in the context of g2t generation, we are concerned not only with the fluency of the gener-ated text, but also with its faithfulness to the input graph. while recent models such as t5 and gpt models are very fluent, they have been criticized for their factual accuracy, a problem commonly referred to as hallucination @xcite . hallucinations are a serious drawback in g2t, where the generated text should only contain facts mentioned in the input graph. in this work, we focus on measuring and improving the factual accuracy of g2t generative models. more precisely, our contributions are as follows: i) we introduce a novel metric factspotter for detecting if g2t generations are faithful to the facts present in the input graph; ii) we show how factspotter can be used in the inference step of any g2t model to improve its generations; iii) we analyze the difficulty of existing g2t datasets and determine which are (resp., are no longer) challenging for state-of-the-art models. factspotter can be extended to other data-to-text tasks via methods for transforming a relational dataset into rdf, such as the r2rml language foot_0 . 2 related work ## problem statement a knowledge graph (kg) consists of facts (or data triples) of the form ⟨subject, relation, object/literal⟩ and/or an ontology describing the properties that hold between classes and/or relations. ## evaluating graph-to-text generation we investigate our first research question (rq1): which metrics would better correlate with the fac-tual faithfulness of the generated text? for this question, we compare factspotter with: bleu @xcite , meteor @xcite , bertscore @xcite , bleurt @xcite , bartscore @xcite . the only metric that is not normalized is bartscore. this metric has a variant specifically adapted for factual faithfulness, bartscorefaithful. further details of these metrics are provided in appendix a.1. we calculate the system-level correlation between automatic metrics and human annotations. given s systems under evaluation, for a certain dimension, e.g., fluency, we compute the correlation between the system-level automatic metric scores [m (s 1 ), . . . m (s s )] and the corresponding system-level human scores [h(s 1 ), . . . h(s s )], where m (s i ) is the score of the automatic metric on the texts from system s i , and h(s i ) is the score of the human annotation on the same result. similarly to @xcite , we compute three correlation metrics: pearson correlation (r), spearman correlation (ρ), and kendall's tau (τ ). to test if a metric m 1 has a higher correlation with human annotations than m 2 , we use the bootstrapping technique proposed in (wilcox, 2016), which we describe in the appendix a.2. we also report the sentence-level correlation in appendix a.5. in addition to automatic measures, we report the correlation between one annotator and the average score of the remaining annotators, which should be a upper bound on the correlation we can obtain using automatic measures. webnlg 2017. in the webnlg 2017 challenge @xcite , the organizers annotated 9 submissions on semantic adequacy (the text correctly represents the meaning in the data), text structure (as above, referred in the original paper as grammar) and fluency (as above). this annotation has carried over 223 samples. webnlg 2020. after the webnlg 2020 challenge @xcite , the organizers annotated the 16 participating systems on data correctness (the predicates found in the data are correctly mentioned together with their subject and object), data coverage (the text includes descriptions of all predicates presented in the data), and relevance (the text describes only those predicates with related subjects and objects which are in the data), in addition to text structure (the text is grammati results. on webnlg 2020, ## improving the factual faithfulness of graph-to-text generation in this section, we investigate the answer to our third research question: can we improve graph-totext generation on factual faithfulness (rq2)? for this, we first explain how to improve the inference step of any g2t model using factspotter, and then we present the results of this technique on the stateof-the-art models for g2t generation. ## remaining challenges in g2t task finally, we consider (rq3): is g2t task solved on existing datasets? we have observed high factspotter scores in section 6 on the performances of models. we use factspotter to do a more detailed analysis: we investigate what is the percentage of generations in each dataset which had at least a fact considered missing by factspotter. a fact is considered as missing if the score of the pair (fact, generated sentence) is lower than 0.5. we obtain the following statistics: 1.94% of texts miss a fact in simplequestions; 7.27% of texts miss at least a fact in dart; 5.79% of webnlg 2017 texts miss at least one fact, and 12.64% for webnlg 2020; for grailqa we have 5.8% for the zero shot split, 4.36% for the compositional split and 1.13 for the iid split. according to the observations, webnlg 2020 is the most challenging dataset, followed by dart, the zero shot split of grailqa, and the webnlg 2017 dataset. in appendix a.7, taking grailqa and webnlg 2017 as examples, we analysed the difficulty of g2t on datasets from different knowledge graphs, by looking into how often predicates and entity names are rephrased or expressed exactly as in the input graph. we perform a second evaluation, this time by manually analyzing the output of the models. we consider the worst 20 sentences according to bleurt and factspotter, hence 40 examples per dataset or split. on simplequestions, the generations are fluent, however 22/40 have an incorrect predicate. for grailqa, in the iid split the predicates are correctly generated, but the models still have difficulties in generating some entity names (16/40). for the zero-shot split, generations suffer from wrong entities and predicates (22/40). the compositional split has several samples with wrong ground truth (6/40 of the worst generations) and 19 out of 40 incorrect generations. for dart, 24 generations are not correct. on webnlg 2017, from the worst 40 generations, only two might benefit from improved fluency, while in many examples, the generated sentence was more fluent than the ground truth (14/40). regarding correctness, only 2 out of 40 generations had a missing triple, while two generations incorrectly used a predicate. on webnlg2020, only one instance exhibits room for improvement in fluency, but 24 instances either omit factual information or contain incorrect facts. among the 20 outputs with the lowest bleurt scores, 9 are rephrased texts with correctly explained facts. in contrast, among the 20 outputs with the lowest factspotter, only 4 instances fall into this category. based on our manual annotations, we observed that models are able to produce correct generations. however, when the generation is rephrased in respect to the ground truth sentence, metrics like bleurt, which measure if two sentences are equivalent, struggle to assign high scores. we recall that bleurt, a normalized metric, gives a score of 1 to equivalent sentences. on the dataset webnlg 2017, our metric assigns a very high score to the models, while the highest average bleurt score is 73.44%. the bleurt scores of the generations vary from 0.46 to 0.99; more than 50% of the test set generations score less than 0.80. we sampled 40 generations with bleurt @xmath0.8 and note that 35 generations are correct, which are rephrasing the ground truth, while 2 out of 35 that are better than the ground truth. hence, we observe that bleurt score cannot be used to determine if we have achieved a good performance on a dataset, it can only be used to compare different models. this issue has also been pointed out by the authors foot_1 . factspotter answers whether a fact is present in text; it does not have to address the much harder task of deciding if two sentences are equivalent. besides being more reliable because it is solving a simpler task, it is also more interpretable as we can investigate the exact triples that are classified as negative, instead of doing a complete comparison between a subgraph and a sentences or between two sentences. this is especially useful for long input graphs and long generations. ## conclusion in this work, we have presented a new metric for measuring factual faithfulness in g2t, factspotter. this metric can be trained in a self supervised fashion, using the same annotations as a g2t model. we have shown it achieves the highest correlation with humans on factual faithfulness and it can be used as a plug-in feature for g2t models. finally, we have used our metric to analyze the difficulty of existing datasets. we have observed that models perform very well on these datasets, hence new datasets should be proposed. such datasets could cover more difficult input graphs, for example triple from tables. in addition, through the initiative of governments to release tabular data related to public interest foot_2 , tools trained to express in natural language the content of tables could be used as user friendly interfaces for citizens.