Building Foundations for Inclusiveness through Expert-Annotated Data Moreno La Quatra1,† , Salvatore Greco2,*,† , Luca Cagliero2 , Michela Tonti3 , Francesca Dragotto4 , Rachele Raus5 , Stefania Cavagnoli4 and Tania Cerquitelli2 1 Kore University of Enna, Enna, Italy 2 Politecnico di Torino, Turin, Italy 3 Università degli studi di Bergamo, Bergamo, Italy 4 Università degli Studi di Roma Tor Vergata, Rome, Italy 5 Università di Bologna, Bologna, Italy Abstract Natural Language Understanding and Generation models suffer from a limited capability of understanding the nuances of inclusive communication as they are trained on massive data, often including significant portions of non-inclusive content. Even when the models are specifically designed to address non-inclusive language detection or reformulation, they disregard, to a large extent, inclusiveness- related features that are likely correlated with the inclusive language nuances, such as the discourse type, level of inclusiveness, and intended context of use. To assess the importance of additional inclusiveness-related features, we collect a new corpus of Italian administrative documents humanly annotated by linguistic experts. Linguistic experts not only highlight non-inclusive text snippets and propose possible reformulations, but also annotate multi-aspect labels related to different inclusive language nuances. We empirically show that a multi-task learning approach that leverages the multi-aspect annotations can improve the non-inclusive text reformulation performance, thereby confirming the potential of expert-annotated data in inclusive language processing. Keywords inclusive language, natural language processing, text generation, deep learning 1. Introduction tially different: while language editing tools rewrite parts of the source text based on predefined expert-provided rules, Non-inclusive expressions are widespread in humanly writ- Natural Language Understanding and Generation models ten documents [1]. Training Natural Language Understand- can leverage annotations to capture the nuances of anno- ing and Generation models on massive data exposes them tated text in a self-supervised manner. The use of textual to bias issues related to language inclusiveness. Addressing annotations also relieves annotators of the task of explicitly this issue is particularly relevant because Artificial Intel- formulating or adhering to ad hoc linguistic rules. ligence (AI)-based solutions must be used responsibly to In the context of inclusive language understanding and correctly model inclusive language practices and not unin- generation, most of the previous work exploits rule-based tentionally marginalize or disadvantage certain groups. or round-trip translations to annotate texts for inclusivity is- To mitigate the presence of bias in data, applications based sues [9, 10, 11, 12]. However, these works often overlook the on AI rely on human supervision for model training and significance of human expert annotations, opting instead post-processing evaluation. This is quite common in the for rule-based approaches or artificially created datasets areas of Natural Language Understanding and Generative AI, generated through round-trip translations. The role of lin- in which applications like Large Language Models (LLMs) guistic annotators in providing specific understanding and provide end-users with conversational and language editing annotations of language data is crucial for developing more services [2]. inclusive AI models [13, 14]. The computational linguistic community has agreed A limited body of work has been devoted to generating on the need to leverage human expert annotations in and exploiting multi-faceted expert human annotations to experience-based learning for bias detection and mitiga- drive AI models for inclusive language, e.g., [15, 16, 17]. tion [3, 4, 5, 6]. However, the linguistics literature often However, existing benchmarks of annotated text for inclu- underestimates the importance of linguistic annotators be- sive language processing neglect potentially relevant as- cause of the widespread tendency to value the figures of pre- pects such as the level of inclusiveness, the intended context and post-editors [7, 8]. Editing and annotation are substan- of use, and the text genre. These aspects have the poten- tial to improve the inclusive language understanding and Published in the Proceedings of the Workshops of the EDBT/ICDT 2024 generation capabilities of AI models. Joint Conference (March 25-28, 2024), Paestum, Italy This paper proposes an expert-annotated dataset cover- * Corresponding author. ing these new aspects and investigates their usefulness in † These authors contributed equally. enhancing the performance of the task of non-inclusive text $ moreno.laquatra@unikore.it (M. La Quatra); salvatore_greco@polito.it (S. Greco); luca.cagliero@polito.it reformulation in the absence of rule-based editing models. (L. Cagliero); michela.tonti@unibg.it (M. Tonti); To this end, we enrich a corpus of Italian administrative francesca.dragotto@gmail.com (F. Dragotto); rachele.raus@unibo.it documents with multi-aspect annotations, providing more (R. Raus); stefania.cavagnoli@uniroma2.it (S. Cavagnoli); insights into the inclusive language nuances. The purpose is tania.cerquitelli@polito.it (T. Cerquitelli) to enable the study of new features describing inclusiveness € https://www.mlaquatra.me/ (M. La Quatra); https://grecosalvatore.github.io/ (S. Greco) aspects neglected by existing approaches, such as the level of  0000-0001-8838-064X (M. La Quatra); 0000-0001-7239-9602 inclusiveness, register, and genre. By enriching the language (S. Greco); 0000-0002-7185-5247 (L. Cagliero); 0000-0001-5306-3054 descriptions with new inclusiveness-related features, we (R. Raus); 0000-0002-9039-6226 (T. Cerquitelli) provide the research community with new resources to © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribu- tion 4.0 International (CC BY 4.0). CEUR ceur-ws.org Workshop ISSN 1613-0073 Proceedings enhance the understanding and writing capabilities of AI- By learning language inclusiveness patterns from a di- based solutions. versified, context-dependent set of expert annotations, AI We also collect preliminary results on the use of multi- models gain exposure to subtle interpretive differences. The aspect annotations in a multi-task learning approach to consistency across annotations is ensured through detailed enhance non-inclusive language reformulations. The results guidelines and instructions provided to experts. Before full confirm the potential of the inclusiveness-related expert annotation, a collaborative analysis of a sample set identifies annotations. any divergent interpretations to refine guidance. Statistics on annotated data. Table 1 reports the num- 2. The annotation process ber of annotated sentences for each aspect, separately for The term annotation is often used to indicate the process the training, validation, and test sets. by which textual data are subjected to a tightly interrelated two-phase activity [6]: a) Identification, selection, and lo- Task ID Train Validation Test calisation of specific documents, and b) Interpretation and NILR 6491 956 579 labeling of those documents. The first phase entails identify- ILC 9207 1421 866 ing and detailing the text segments that exhibit the linguistic RC 2167 338 247 phenomenon under investigation. Subsequently, in the in- GC 2166 338 248 terpretation phase, the selected occurrences are humanly Table 1 labeled. These annotations may encompass various forms Statistics on data. NILR=Non-Inclusive Language Reformulation, ranging from a selection of pre-established alternatives to ILC=Inclusiveness Level Classification, RC=Register Classifica- free-text comments or possible reformulations. tion, GC=Genre Classification. Unlike human annotators, AI models often lack cognitive abilities such as common sense reasoning and generaliza- tion capabilities due to the relatively limited numbers of linguistic examples used for model training compared to Example of annotations. Table 2 shows an example of the impressive variety of natural language forms. an Italian annotated sentence (as well as the correspond- Human annotators need sufficient expertise to interpret ing English translation for non-Italian readers). Linguistics nuanced linguistic phenomena and assign appropriate labels experts assign different annotations to each sentence. In adequately. Their annotations are at the base of a supervised this example, they have assigned three labels to the sen- learning process. The trained models can progressively tence. Regarding inclusiveness, the sentence has been cate- learn from annotated data as automatized humans do, but gorized as non-inclusive because it contains “Il Presidente” at a scale not possible through manual work alone. (i.e., Chair/President) and “Rettore” (i.e., Rector), which are masculine declensions of professional roles. In addition, the sentence also contains “suo decreto”, which refers to a de- Annotation of Italian administrative documents. We cree that comes from a male person, so the sentence is not have designed and utilized a novel benchmark dataset for in- inclusive. The discourse sequence is of the administrative clusive language writing in Italian. This dataset comprises type, as the content refers to an administrative topic, and administrative communications sourced from the Italian the used language is specialized, as the content describes public administration, spanning across both national and specific and technical aspects. regional levels. We annotate the corpus at the sentence level. To this end, we set up a heterogeneous team of 13 linguistic experts with diverse experiences and expertise 3. Case study: Leveraging Aspects in inclusive language. The team consists of predominantly female individuals, all native Italian speakers. All the anno- for Italian Inclusive Language tators are educated: 57% have at least 10 years of experience Reformulation in linguistics, and 50% have at least 3 years of experience in inclusive language. In addition, the annotators received, We conduct an empirical analysis to examine the impact on average, about 30 hours of training specific to inclusive of utilizing expert annotations in inclusive language gen- language annotations. eration. Specifically, we investigate the advantages of si- Each human annotator independently assigns multaneously addressing two key objectives: reformulating inclusiveness-related metadata to the document sen- non-inclusive language and predicting various aspects of tences. Each sentence can be enriched with multiple inclusiveness. annotations. The annotations consist of (a) The refor- mulation of any non-inclusive piece of text, i.e., an Tasks. Given a non-inclusive piece of text 𝑇 , the Non- alternative inclusive form; (b) The level of inclusiveness Inclusive Language Reformulation (NILR) task aims at gen- of the input sentence indicating whether a sentence is erating an equivalent inclusive natural language form. The non-inclusive, inclusive, or not pertinent; (c) The register NILR task is a sequence-to-sequence problem, where the or intended context of use, i.e., Standard, Specialized, or input is a non-inclusive sentence and the output is the cor- Informative/Educational; (d) the discourse type or genre, i.e., responding inclusive sentence. Legal, Administrative, Technical, or Informative/Educational. Given 𝑇 and an aspect 𝐴, the goal is to predict the 𝐴’s Additional contextual aspects could be included in future value for 𝑇 . 𝐴 can be the level of inclusiveness, register annotations to enhance models’ understanding of inclusive or intended context of use, and discourse type or genre. language usage further. By jointly providing those anno- According to the aspect under analysis, the corresponding tations, the experts aimed to capture inclusive language’s sub-tasks are denoted by Inclusiveness Level Classification nuanced, multi-faceted nature. (ILC), Register Classification (RC), and Genre Classification Inclusive Discursive Clear Sentence Reformulation Class Sequence Language "Chi ricopre la carica di Presidente, su "Il Presidente, scelto dal Rettore tra scelta di chi riveste il ruolo di Rettore i professori ordinari dell’Ateneo con tra il personale docente ordinario competenze in ambito di valutazione, IT dell’Ateneo con competenze in ambito Non-inclusivo Amministrativo Specialistico accreditamento e qualità e nominato di valutazione, accreditamento e qualità con suo decreto, previo parere del e in seguito a nomina con suo decreto, Senato Accademico;" previo parere del Senato Accademico;" "The Chair/President, selected by the "Who serves as Chair/President, Rector among the full professors of selected by who holds the position the University, with expertise in the of Rector, among the full professors of EN fields of evaluation, accreditation, the University with expertise in the fields Non-inclusive Administrative Specialized and quality and appointed by his of evaluation, accreditation, and quality decree, subject to the opinion of the and appointed by his or her decree, subject Academic Senate;" to the opinion of the Academic Senate;" Table 2 Example of sentence annotations illustrating non-inclusive language reformulation in Italian (IT) and English (EN), along with corresponding inclusiveness classification, discursive sequence, and clear language class. Setting R-1 R-2 R-L Human Eval between the two pieces of text. The larger the score, the Single-Task 74.95 64.09 74.79 0.67 higher the syntactic similarity. R-1, R-2, and R-L count the Multi-Task 75.58 64.37 75.36 0.70 unit overlap in terms of unigrams, bigrams, and longest common subsequences, respectively. Table 3 To complement the quantitative evaluation, we also per- Performance comparison between Single- and Multi-task Learn- form a qualitative evaluation of the achieved results. We ing approaches in inclusive language generation, evaluated based involved six human evaluators who were asked to label on ROUGE scores (R-1, R-2, R-L) and human evaluation. each model-generated sentence as: correct if it accurately maintained the original meaning while using inclusive lan- guage appropriately for the context; partially correct if some (GC). The ILC, RC, and GC tasks are treated as separate aspects were reformed correctly, but others were missed classification problems, where the input is a sentence and or inaccurate; or not correct if the rewriting fundamentally the output is the corresponding aspect value. failed to capture the original meaning or usage intention. This multi-level feedback aims at capturing the models’ abil- Single- vs. Multi-Task Learning To compare the per- ity to perform the rewriting task sensitively across different formance of models trained using different learning ap- scenarios beyond just string-matching metrics. proaches, we conducted experiments in both single-task To each reformulation, we assign a score to each anno- and multi-task learning settings. tation as follows: 1 for correct, 0.5 for partially correct, and In Single-Task Learning, we exclusively focus on the task 0 for incorrect. The final score for each reformulation is of Non-Inclusive Language Reformulation (NILR), disregard- computed as the average over all the expert annotations ing all aspect-related annotations. We leverage an encoder- (𝑚 = 6). Finally, we average the scores for all the reformu- decoder architecture, specifically BART-IT [18], which is a lations (𝑛 = 30) to obtain a single score for each model. BART architecture [19] pre-trained on a clean Italian corpus [20]. The model is fine-tuned on the NILR task with the Results’ overview. Columns 2, 3, and 4 in Table 3 show twofold objective of modifying the input sentence to make the ROUGE scores for both models. The multi-task learn- it inclusive while maintaining the original meaning. ing achieves the best performance on all the quantitative Conversely, in Multi-Task Learning, we integrate the NILR metrics. Regarding the human evaluation, we obtained 6 task with Aspect Classification tasks during training (i.e., annotations for 30 reformulations for each model. For the ILC, RC, and GC). For the additional tasks, we specifically model trained with the single task configuration, 93 were leverage the encoder component of the model, which ex- correct, 55 were partially correct, and 32 were incorrect. tracts representations of the input text. The encoder com- Instead, for the multi-task model, 101 were correct, 49 were ponent is additionally trained with a classification objective. partially correct, and 30 were incorrect. Column 5 reports Each task is associated with a separate classification head, the average human evaluation scores for both models. The trained to predict the corresponding aspect value for the human scores are coherent with the quantitative ones, show- input sentence. By interleaving these tasks during training, ing that the model trained under multi-task settings bene- the model learns to simultaneously address NILR and create fits from the additional labels. Based on these preliminary encoder representations that capture various aspects related results, we can conclude that the nuanced and multidimen- to inclusiveness. sional annotations of inclusive language have the potential to develop a more comprehensive approach to modeling Evaluation Metrics. We evaluate the quality of the text inclusive language. reformulation using a standard train-validation-test split on our expert-annotated data. To compare the automati- cally generated and expected reformulations, we use the established ROUGE F1-scores [21]. They measure the unit overlap, in terms of the number of n-grams in common, 4. Conclusions [2] A. Balayn, J. Yang, Z. Szlávik, A. 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