ymoslem/acl-abstracts huggingface dataset:
Revisiting text decomposition methods for NLI-based factuality scoring of summaries (2022)

Scoring the factuality of a generated summary involves measuring the degree to which a target text contains factual information using the input document as support. Given the similarities in the problem formulation, previous work has shown that Natural Language Inference models can be effectively repurposed to perform this task. As these models are trained to score entailment at a sentence level, several recent studies have shown that decomposing either the input document or the summary into sentences helps with factuality scoring. But is fine-grained decomposition always a winning strategy? In this paper we systematically compare different granularities of decomposition - from document to sub-sentence level, and we show that the answer is no. Our results show that incorporating additional context can yield improvement, but that this does not necessarily apply to all datasets. We also show that small changes to previously proposed entailment-based scoring methods can result in better performance, highlighting the need for caution in model and methodology selection for downstream tasks.
Semantic Similarity as a Window into Vector- and Graph-Based Metrics (2022)

In this work, we use sentence similarity as a lens through which to investigate the representation of meaning in graphs vs. vectors. On semantic textual similarity data, we examine how similarity metrics based on vectors alone (SENTENCE-BERT and BERTSCORE) fare compared to metrics based on AMR graphs (SMATCH and S2MATCH). Quantitative and qualitative analyses show that the AMR-based metrics can better capture meanings dependent on sentence structures, but can also be distracted by structural differences{---}whereas the BERT-based metrics represent finer-grained meanings of individual words, but often fail to capture the ordering effect of words within sentences and suffer from interpretability problems. These findings contribute to our understanding of each approach to semantic representation and motivate distinct use cases for graph and vector-based representations.
Towards In-Context Non-Expert Evaluation of Reflection Generation for Counselling Conversations (2022)

Reflection is an essential counselling strategy, where the therapist listens actively and responds with their own interpretation of the client`s words. Recent work leveraged pre-trained language models (PLMs) to approach reflection generation as a promising tool to aid counsellor training. However, those studies used limited dialogue context for modelling and simplistic error analysis for human evaluation. In this work, we take the first step towards addressing those limitations. First, we fine-tune PLMs on longer dialogue contexts for reflection generation. Then, we collect free-text error descriptions from non-experts about generated reflections, identify common patterns among them, and accordingly establish discrete error categories using thematic analysis. Based on this scheme, we plan for future work a mass non-expert error annotation phase for generated reflections followed by an expert-based validation phase, namely {\textquotedblleft}whether a coherent and consistent response is a good reflection{\textquotedblright}.
WikiOmnia: filtration and evaluation of the generated QA corpus on the whole Russian Wikipedia (2022)

The General QA field has been developing the methodology referencing the Stanford Question answering dataset (SQuAD) as the significant benchmark. Compiling factual questions datasets requires manual annotations, limiting the training data`s potential size. We present the WikiOmnia dataset, a new publicly available set of QA pairs and corresponding Russian Wikipedia article summary sections, composed with a fully automated generation and filtration pipeline. To ensure high quality of generated QA pairs, diverse manual and automated evaluation techniques were applied. The WikiOmnia pipeline is available open-source and is also tested for creating SQuAD-formatted QA on other domains, like news texts, fiction, and social media. The resulting dataset includes two parts: raw data on the whole Russian Wikipedia (7,930,873 QA pairs with paragraphs for ruGPT-3 XL and 7,991,040 QA pairs with paragraphs for ruT5-large) and cleaned data with strict automatic verification (over 160,000 QA pairs with paragraphs for ruGPT-3 XL and over 3,400,000 QA pairs with paragraphs for ruT5-large).
Evaluation of Response Generation Models: Shouldn`t It Be Shareable and Replicable? (2022)

Human Evaluation (HE) of automatically generated responses is necessary for the advancement of human-machine dialogue research. Current automatic evaluation measures are poor surrogates, at best. There are no agreed-upon HE protocols and it is difficult to develop them. As a result, researchers either perform non-replicable, non-transparent and inconsistent procedures or, worse, limit themselves to automated metrics. We propose to standardize the human evaluation of response generation models by publicly sharing a detailed protocol. The proposal includes the task design, annotators recruitment, task execution, and annotation reporting. Such protocol and process can be used as-is, as-a-whole, in-part, or modified and extended by the research community. We validate the protocol by evaluating two conversationally fine-tuned state-of-the-art models (GPT-2 and T5) for the complex task of personalized response generation. We invite the community to use this protocol - or its future community amended versions - as a transparent, replicable, and comparable approach to HE of generated responses.
Enhancing and Evaluating the Grammatical Framework Approach to Logic-to-Text Generation (2022)

Logic-to-text generation is an important yet underrepresented area of natural language generation (NLG). In particular, most previous works on this topic lack sound evaluation. We address this limitation by building and evaluating a system that generates high-quality English text given a first-order logic (FOL) formula as input. We start by analyzing the performance of Ranta (2011)`s system. Based on this analysis, we develop an extended version of the system, which we name LoLa, that performs formula simplification based on logical equivalences and syntactic transformations. We carry out an extensive evaluation of LoLa using standard automatic metrics and human evaluation. We compare the results against a baseline and Ranta (2011)`s system. The results show that LoLa outperforms the other two systems in most aspects.
Controllable Text Generation for All Ages: Evaluating a Plug-and-Play Approach to Age-Adapted Dialogue (2022)

To be trusted and perceived as natural and coherent, conversational systems must adapt to the language of their users. While personalized dialogue is a promising direction, controlling generation for fine-grained language features remains a challenge in this approach. A recent line of research showed the effectiveness of leveraging pre-trained language models toward adapting to a text`s topic or sentiment. In this study, we build on these approaches and focus on a higher-level dimension of language variation: speakers' age. We frame the task as a dialogue response generation, and test methods based on bag-of-words (BoW) and neural discriminators (Disc) to condition the output of GPT-2 and DialoGPT without altering the parameters of the language models. We show that Disc models achieve a higher degree of detectable control than BoW models based on automatic evaluation. In contrast, humans can partially detect age differences in BoW but not Disc responses. Since BoW responses are deemed better than Disc ones by humans, simple controllable methods thus appear to be a better tradeoff between adaptation and language quality. Our work confirms the challenges of adapting to higher-level dimensions of language variation. Moreover, it highlights the need to evaluate natural language generation thoroughly.
Template-based Contact Email Generation for Job Recommendation (2022)

Text generation has long been a popular research topic in NLP. However, the task of generating contact emails from recruiters to candidates in the job recommendation scenario has received little attention by the research community. This work aims at defining the topic of automatic email generation for job recommendation, identifying the challenges, and providing a baseline template-based solution for Danish jobs. Evaluation by human experts shows that our method is effective. We wrap up by discussing the future research directions for better solving this task.
Are Abstractive Summarization Models truly \textquoteleftAbstractive'? An Empirical Study to Compare the two Forms of Summarization (2022)

Automatic Text Summarization has seen a large paradigm shift from extractive methods to abstractive (or generation-based) methods in the last few years. This can be attributed to the availability of large autoregressive language models that have been shown to outperform extractive methods. In this work, we revisit extractive methods and study their performance against state of the art(SOTA) abstractive models. Through extensive studies, we notice that abstractive methods are not yet completely abstractive in their generated summaries. In addition to this finding, we propose an evaluation metric that could benefit the summarization research community to measure the degree of abstractiveness of a summary in comparison to their extractive counterparts. To confirm the generalizability of our findings, we conduct experiments on two summarization datasets using five powerful techniques in extractive and abstractive summarization and study their levels of abstraction.
Transfer learning for multilingual vacancy text generation (2022)

Writing job vacancies is a repetitive and expensive task for humans. This research focuses on automatically generating the benefit sections of vacancies at redacted from job attributes using mT5, the multilingual version of the state-of-the-art T5 transformer trained on general domains to generate texts in multiple languages. While transformers are accurate at generating coherent text, they are sometimes incorrect at including the structured data (the input) in the generated text. Including the input correctly is crucial for vacancy text generation; otherwise, the candidates may get misled. To evaluate how the model includes the input we developed our own domain-specific metrics (input generation accuracy). This was necessary, because Relation Generation, the pre-existing evaluation metric for data-to-text generation uses only string matching, which was not suitable for our dataset (due to the binary field). With the help of the new evaluation method we were able to measure how well the input is included in the generated text separately for different types of inputs (binary, categorical, numeric), offering another contribution to the field. Additionally, we also evaluated how accurate the mT5 model generates the text in the requested language. The results show that mT5 is very accurate at generating the text in the correct language, at including seen categorical inputs and binary values correctly in the generated text. However, mT5 performed worse when generating text from unseen city names or working with numeric inputs. Furthermore, we found that generating additional synthetic training data for the samples with numeric input can increase the input generation accuracy, however this only works when the numbers are integers and only cover a small range.
Plug-and-Play Recipe Generation with Content Planning (2022)

Recent pre-trained language models have shown promising capability to generate fluent and realistic natural text. However, generating multi-sentence text with global content planning has been a long-existing research question. The current controlled text generation models cannot directly address this issue, as they usually condition on single known control attribute. We propose a low-cost yet effective framework that explicitly models content plans and optimizes the joint distribution of the natural sequence and the content plans in a plug-and-play post-processing manner. We evaluate our model with extensive automatic metrics and human evaluations and show that it achieves the state-of-the-art performance on the recipe generation task on Recipe1M+ dataset.
Towards Attribute-Entangled Controllable Text Generation: A Pilot Study of Blessing Generation (2022)

Controllable Text Generation (CTG) has obtained great success due to its fine-grained generation ability obtained by focusing on multiple attributes. However, most existing CTG researches overlook how to utilize the attribute entanglement to enhance the diversity of the controlled generated texts. Facing this dilemma, we focus on a novel CTG scenario, i.e., blessing generation which is challenging because high-quality blessing texts require CTG models to comprehensively consider the entanglement between multiple attributes (e.g., objects and occasions). To promote the research on blessing generation, we present EBleT, a large-scale Entangled Blessing Text dataset containing 293K English sentences annotated with multiple attributes. Furthermore, we propose novel evaluation metrics to measure the quality of the blessing texts generated by the baseline models we designed. Our study opens a new research direction for controllable text generation and enables the development of attribute-entangled CTG models.
Unsupervised Token-level Hallucination Detection from Summary Generation By-products (2022)

Hallucinations in abstractive summarization are model generations that are unfaithful to the source document. Current methods for detecting hallucinations operate mostly on noun phrases and named entities, and restrict themselves to the XSum dataset, which is known to have hallucinations in 3 out of 4 training examples (Maynez et al., 2020). We instead consider the CNN/DailyMail dataset where the summarization model has not seen abnormally many hallucinations during training. We automatically detect candidate hallucinations at the token level, irrespective of its part of speech. Our detection comes essentially for free, as we only use information the model already produces during generation of the summary. This enables practitioners to jointly generate a summary and identify possible hallucinations, with minimal overhead. We repurpose an existing factuality dataset and create our own token-level annotations. The evaluation on these two datasets shows that our model achieves better precision-recall tradeoffs than its competitors, which additionally require a model forward pass.
A Corpus and Evaluation for Predicting Semi-Structured Human Annotations (2022)

A wide variety of tasks have been framed as text-to-text tasks to allow processing by sequence-to-sequence models. We propose a new task of generating a semi-structured interpretation of a source document. The interpretation is semi-structured in that it contains mandatory and optional fields with free-text information. This structure is surfaced by human annotations, which we standardize and convert to text format. We then propose an evaluation technique that is generally applicable to any such semi-structured annotation, called equivalence classes evaluation. The evaluation technique is efficient and scalable; it creates a large number of evaluation instances from a comparably cheap clustering of the free-text information by domain experts. For our task, we release a dataset about the monetary policy of the Federal Reserve. On this corpus, our evaluation shows larger differences between pretrained models than standard text generation metrics.
T5QL: Taming language models for SQL generation (2022)

Automatic SQL generation has been an active research area, aiming at streamlining the access to databases by writing natural language with the given intent instead of writing SQL. Current SOTA methods for semantic parsing depend on LLMs to achieve high predictive accuracy on benchmark datasets. This reduces their applicability, since LLMs requires expensive GPUs. Furthermore, SOTA methods are ungrounded and thus not guaranteed to always generate valid SQL. Here we propose T5QL, a new SQL generation method that improves the performance in benchmark datasets when using smaller LMs, namely T5-Base, by 13pp when compared against SOTA methods. Additionally, T5QL is guaranteed to always output valid SQL using a context-free grammar to constrain SQL generation. Finally, we show that dividing semantic parsing in two tasks, candidate SQLs generation and candidate re-ranking, is a promising research avenue that can reduce the need for large LMs.
Human perceiving behavior modeling in evaluation of code generation models (2022)

Within this study, we evaluated a series of code generation models based on CodeGen and GPTNeo to compare the metric-based performance and human evaluation. For a deeper analysis of human perceiving within the evaluation procedure we`ve implemented a 5-level Likert scale assessment of the model output using a perceiving model based on the Theory of Planned Behavior (TPB). Through such analysis, we showed an extension of model assessment as well as a deeper understanding of the quality and applicability of generated code for practical question answering. The approach was evaluated with several model settings in order to assess diversity in quality and style of answer. With the TPB-based model, we showed a different level of perceiving the model result, namely personal understanding, agreement level, and readiness to use the particular code. With such analysis, we investigate a series of issues in code generation as natural language generation (NLG) problems observed in a practical context of programming question-answering with code.
Nearest Neighbor Language Models for Stylistic Controllable Generation (2022)

Recent language modeling performance has been greatly improved by the use of external memory. This memory encodes the context so that similar contexts can be recalled during decoding. This similarity depends on how the model learns to encode context, which can be altered to include other attributes, such as style. We construct and evaluate an architecture for this purpose, using corpora annotated for politeness, formality, and toxicity. Through extensive experiments and human evaluation we demonstrate the potential of our method to generate text while controlling style. We find that style-specific datastores improve generation performance, though results vary greatly across styles, and the effect of pretraining data and specific styles should be explored in future work.
On reporting scores and agreement for error annotation tasks (2022)

This work examines different ways of aggregating scores for error annotation in MT outputs: raw error counts, error counts normalised over total number of words (word percentage'), and error counts normalised over total number of errors (error percentage'). We use each of these three scores to calculate inter-annotator agreement in the form of Krippendorff`s $alpha$ and Pearson`s $r$ and compare the obtained numbers, overall and separately for different types of errors. While each score has its advantages depending on the goal of the evaluation, we argue that the best way of estimating inter-annotator agreement using such numbers are raw counts. If the annotation process ensures that the total number of words cannot differ among the annotators (for example, due to adding omission symbols), normalising over number of words will lead to the same conclusions. In contrast, total number of errors is very subjective because different annotators often perceive different amount of errors in the same text, therefore normalising over this number can indicate lower agreements.
Answerability: A custom metric for evaluating chatbot performance (2022)

Most commercial conversational AI products in domains spanning e-commerce, health care, finance, and education involve a hierarchy of NLP models that perform a variety of tasks such as classification, entity recognition, question-answering, sentiment detection, semantic text similarity, and so on. Despite our understanding of each of the constituent models, we do not have a clear view as to how these models affect the overall platform metrics. To bridge this gap, we define a metric known as answerability, which penalizes not only irrelevant or incorrect chatbot responses but also unhelpful responses that do not serve the chatbot`s purpose despite being correct or relevant. Additionally, we describe a formula-based mathematical framework to relate individual model metrics to the answerability metric. We also describe a modeling approach for predicting a chatbot`s answerability to a user question and its corresponding chatbot response.
Improved Evaluation of Automatic Source Code Summarisation (2022)

Source code summaries are a vital tool for the understanding and maintenance of source code as they can be used to explain code in simple terms. However, source code with missing, incorrect, or outdated summaries is a common occurrence in production code. Automatic source code summarisation seeks to solve these issues by generating up-to-date summaries of source code methods. Recent work in automatically generating source code summaries uses neural networks for generating summaries; commonly Sequence-to-Sequence or Transformer models, pretrained on method-summary pairs. The most common method of evaluating the quality of these summaries is comparing the machine-generated summaries against human-written summaries. Summaries can be evaluated using n-gram-based translation metrics such as BLEU, METEOR, or ROUGE-L. However, these metrics alone can be unreliable and new Natural Language Generation metrics based on large pretrained language models provide an alternative. In this paper, we propose a method of improving the evaluation of a model by improving the preprocessing of the data used to train it, as well as proposing evaluating the model with a metric based off a language model, pretrained on a Natural Language (English) alongside traditional metrics. Our evaluation suggests our model has been improved by cleaning and preprocessing the data used in model training. The addition of a pretrained language model metric alongside traditional metrics shows that both produce results which can be used to evaluate neural source code summarisation.
Most NLG is Low-Resource: here`s what we can do about it (2022)

Many domains and tasks in natural language generation (NLG) are inherently {\textquoteleft}low-resource', where training data, tools and linguistic analyses are scarce. This poses a particular challenge to researchers and system developers in the era of machine-learning-driven NLG. In this position paper, we initially present the challenges researchers {\&} developers often encounter when dealing with low-resource settings in NLG. We then argue that it is unsustainable to collect large aligned datasets or build large language models from scratch for every possible domain due to cost, labour, and time constraints, so researching and developing methods and resources for low-resource settings is vital. We then discuss current approaches to low-resource NLG, followed by proposed solutions and promising avenues for future work in NLG for low-resource settings.