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Register a new userEvaluation Guidelines to Deal with Implicit Phenomena to Assess Factuality in Data-to-Text Generation
إرشادات التقييم للتعامل مع الظواهر الضمنية لتقييم التوظيف في جيل البيانات إلى النص
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يتم تدريب أنظمة توليد البيانات إلى النص على مجموعات البيانات الكبيرة، مثل Webnlg أو RO-Towire أو E2E أو DART. ما وراء مقاييس تقييم الرمز المميز التقليدي (بلو أو نيزك)، فإن القلق الرئيسي الذي يواجهه المولدات الأخيرة هو السيطرة على واقعية النص الذي تم إنشاؤه فيما يتعلق بمواصفات بيانات الإدخال. نبلغ عن تجربتنا عند تطوير نظام تقييم واقعي للوصول التوظيف لتوليد البيانات إلى النص الذي نختبره على بيانات Webnlg و E2E. نحن نهدف إلى إعداد بيانات ذهبية تفوحية يدويا لتحديد الحالات التي ينقل فيها النص معلومات أكثر مما يبرره على أساس البيانات قيد التشغيل (إضافي) أو فشل في توصيل البيانات التي تعد جزءا من الإدخال (مفقود). أثناء تحليل العينات المرجعية (البيانات والنص)، واجهنا مجموعة من عدم اليقين المنهجي المرتبط بالحالات المتعلقة بالظواهر الضمنية في النص، وطبيعة المعرفة غير اللغوية نتوقع أن نشارك عند تقييم الواقعية. ونحن نستمد من خبرتنا مجموعة من المبادئ التوجيهية التقييم للوصول إلى اتفاق مرتفع فيما يتعلق بالمعقيقات بشأن هذه الحالات.
Data-to-text generation systems are trained on large datasets, such as WebNLG, Ro-toWire, E2E or DART. Beyond traditional token-overlap evaluation metrics (BLEU or METEOR), a key concern faced by recent generators is to control the factuality of the generated text with respect to the input data specification. We report on our experience when developing an automatic factuality evaluation system for data-to-text generation that we are testing on WebNLG and E2E data. We aim to prepare gold data annotated manually to identify cases where the text communicates more information than is warranted based on the in-put data (extra) or fails to communicate data that is part of the input (missing). While analyzing reference (data, text) samples, we encountered a range of systematic uncertainties that are related to cases on implicit phenomena in text, and the nature of non-linguistic knowledge we expect to be involved when assessing factuality. We derive from our experience a set of evaluation guidelines to reach high inter-annotator agreement on such cases.
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Abstract Recent approaches to data-to-text generation have adopted the very successful encoder-decoder architecture or variants thereof. These models generate text that is fluent (but often imprecise) and perform quite poorly at selecting appropriate
content and ordering it coherently. To overcome some of these issues, we propose a neural model with a macro planning stage followed by a generation stage reminiscent of traditional methods which embrace separate modules for planning and surface realization. Macro plans represent high level organization of important content such as entities, events, and their interactions; they are learned from data and given as input to the generator. Extensive experiments on two data-to-text benchmarks (RotoWire and MLB) show that our approach outperforms competitive baselines in terms of automatic and human evaluation.
We present DART, an open domain structured DAta Record to Text generation dataset with over 82k instances (DARTs). Data-to-text annotations can be a costly process, especially when dealing with tables which are the major source of structured data and
contain nontrivial structures. To this end, we propose a procedure of extracting semantic triples from tables that encodes their structures by exploiting the semantic dependencies among table headers and the table title. Our dataset construction framework effectively merged heterogeneous sources from open domain semantic parsing and spoken dialogue systems by utilizing techniques including tree ontology annotation, question-answer pair to declarative sentence conversion, and predicate unification, all with minimum post-editing. We present systematic evaluation on DART as well as new state-of-the-art results on WebNLG 2017 to show that DART (1) poses new challenges to existing data-to-text datasets and (2) facilitates out-of-domain generalization. Our data and code can be found at https://github.com/Yale-LILY/dart.
Short-answer scoring is the task of assessing the correctness of a short text given as response to a question that can come from a variety of educational scenarios. As only content, not form, is important, the exact wording including the explicitness
of an answer should not matter. However, many state-of-the-art scoring models heavily rely on lexical information, be it word embeddings in a neural network or n-grams in an SVM. Thus, the exact wording of an answer might very well make a difference. We therefore quantify to what extent implicit language phenomena occur in short answer datasets and examine the influence they have on automatic scoring performance. We find that the level of implicitness depends on the individual question, and that some phenomena are very frequent. Resolving implicit wording to explicit formulations indeed tends to improve automatic scoring performance.
This paper describes our contribution to the Shared Task ReproGen by Belz et al. (2021), which investigates the reproducibility of human evaluations in the context of Natural Language Generation. We selected the paper Generation of Company descriptio
ns using concept-to-text and text-to-text deep models: data set collection and systems evaluation'' (Qader et al., 2018) and aimed to replicate, as closely to the original as possible, the human evaluation and the subsequent comparison between the human judgements and the automatic evaluation metrics. Here, we first outline the text generation task of the paper of Qader et al. (2018). Then, we document how we approached our replication of the paper's human evaluation. We also discuss the difficulties we encountered and which information was missing. Our replication has medium to strong correlation (0.66 Spearman overall) with the original results of Qader et al. (2018), but due to the missing information about how Qader et al. (2018) compared the human judgements with the metric scores, we have refrained from reproducing this comparison.
Due to efficient end-to-end training and fluency in generated texts, several encoder-decoder framework-based models are recently proposed for data-to-text generations. Appropriate encoding of input data is a crucial part of such encoder-decoder model
s. However, only a few research works have concentrated on proper encoding methods. This paper presents a novel encoder-decoder based data-to-text generation model where the proposed encoder carefully encodes input data according to underlying structure of the data. The effectiveness of the proposed encoder is evaluated both extrinsically and intrinsically by shuffling input data without changing meaning of that data. For selecting appropriate content information in encoded data from encoder, the proposed model incorporates attention gates in the decoder. With extensive experiments on WikiBio and E2E dataset, we show that our model outperforms the state-of-the models and several standard baseline systems. Analysis of the model through component ablation tests and human evaluation endorse the proposed model as a well-grounded system.
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