ymoslem/acl-abstracts huggingface dataset:
On the Impact of Data Augmentation on Downstream Performance in Natural Language Processing (2022)

With in the broader scope of machine learning, data augmentation is a common strategy to improve generalization and robustness of machine learning models. While data augmentation has been widely used within computer vision, its use in the NLP has been been comparably rather limited. The reason for this is that within NLP, the impact of proposed data augmentation methods on performance has not been evaluated in a unified manner, and effective data augmentation methods are unclear. In this paper, we look to tackle this by evaluating the impact of 12 data augmentation methods on multiple datasets when finetuning pre-trained language models. We find minimal improvements when data sizes are constrained to a few thousand, with performance degradation when data size is increased. We also use various methods to quantify the strength of data augmentations, and find that these values, though weakly correlated with downstream performance, correlate negatively or positively depending on the task. Furthermore, we find a glaring lack of consistently performant data augmentations. This all alludes to the difficulty of data augmentations for NLP tasks and we are inclined to believe that static data augmentations are not broadly applicable given these properties.
Can Question Rewriting Help Conversational Question Answering? (2022)

Question rewriting (QR) is a subtask of conversational question answering (CQA) aiming to ease the challenges of understanding dependencies among dialogue history by reformulating questions in a self-contained form. Despite seeming plausible, little evidence is available to justify QR as a mitigation method for CQA. To verify the effectiveness of QR in CQA, we investigate a reinforcement learning approach that integrates QR and CQA tasks and does not require corresponding QR datasets for targeted CQA.We find, however, that the RL method is on par with the end-to-end baseline. We provide an analysis of the failure and describe the difficulty of exploiting QR for CQA.
Clustering Examples in Multi-Dataset Benchmarks with Item Response Theory (2022)

In natural language processing, multi-dataset benchmarks for common tasks (e.g., SuperGLUE for natural language inference and MRQA for question answering) have risen in importance. Invariably, tasks and individual examples vary in difficulty. Recent analysis methods infer properties of examples such as difficulty. In particular, Item Response Theory (IRT) jointly infers example and model properties from the output of benchmark tasks (i.e., scores for each model-example pair). Therefore, it seems sensible that methods like IRT should be able to detect differences between datasets in a task. This work shows that current IRT models are not as good at identifying differences as we would expect, explain why this is difficult, and outline future directions that incorporate more (textual) signal from examples.
On the Limits of Evaluating Embodied Agent Model Generalization Using Validation Sets (2022)

Natural language guided embodied task completion is a challenging problem since it requires understanding natural language instructions, aligning them with egocentric visual observations, and choosing appropriate actions to execute in the environment to produce desired changes. We experiment with augmenting a transformer model for this task with modules that effectively utilize a wider field of view and learn to choose whether the next step requires a navigation or manipulation action. We observed that the proposed modules resulted in improved, and in fact state-of-the-art performance on an unseen validation set of a popular benchmark dataset, ALFRED. However, our best model selected using the unseen validation set underperforms on the unseen test split of ALFRED, indicating that performance on the unseen validation set may not in itself be a sufficient indicator of whether model improvements generalize to unseen test sets. We highlight this result as we believe it may be a wider phenomenon in machine learning tasks but primarily noticeable only in benchmarks that limit evaluations on test splits, and highlights the need to modify benchmark design to better account for variance in model performance.
Do Data-based Curricula Work? (2022)

Current state-of-the-art NLP systems use large neural networks that require extensive computational resources for training. Inspired by human knowledge acquisition, researchers have proposed curriculum learning - sequencing tasks (task-based curricula) or ordering and sampling the datasets (data-based curricula) that facilitate training. This work investigates the benefits of data-based curriculum learning for large language models such as BERT and T5. We experiment with various curricula based on complexity measures and different sampling strategies. Extensive experiments on several NLP tasks show that curricula based on various complexity measures rarely have any benefits, while random sampling performs either as well or better than curricula.
The Document Vectors Using Cosine Similarity Revisited (2022)

The current state-of-the-art test accuracy (97.42{\%}) on the IMDB movie reviews dataset was reported by Thongtan and Phienthrakul (2019) and achieved by the logistic regression classifier trained on the Document Vectors using Cosine Similarity (DV-ngrams-cosine) proposed in their paper and the Bag-of-N-grams (BON) vectors scaled by Na{\"i}ve Bayesian weights. While large pre-trained Transformer-based models have shown SOTA results across many datasets and tasks, the aforementioned model has not been surpassed by them, despite being much simpler and pre-trained on the IMDB dataset only. In this paper, we describe an error in the evaluation procedure of this model, which was found when we were trying to analyze its excellent performance on the IMDB dataset. We further show that the previously reported test accuracy of 97.42{\%} is invalid and should be corrected to 93.68{\%}. We also analyze the model performance with different amounts of training data (subsets of the IMDB dataset) and compare it to the Transformer-based RoBERTa model. The results show that while RoBERTa has a clear advantage for larger training sets, the DV-ngrams-cosine performs better than RoBERTa when the labeled training set is very small (10 or 20 documents). Finally, we introduce a sub-sampling scheme based on Na{\"i}ve Bayesian weights for the training process of the DV-ngrams-cosine, which leads to faster training and better quality.
Challenges in including extra-linguistic context in pre-trained language models (2022)

To successfully account for language, computational models need to take into account both the linguistic context (the content of the utterances) and the extra-linguistic context (for instance, the participants in a dialogue). We focus on a referential task that asks models to link entity mentions in a TV show to the corresponding characters, and design an architecture that attempts to account for both kinds of context. In particular, our architecture combines a previously proposed specialized module (an {\textquotedblleft}entity library{\textquotedblright}) for character representation with transfer learning from a pre-trained language model. We find that, although the model does improve linguistic contextualization, it fails to successfully integrate extra-linguistic information about the participants in the dialogue. Our work shows that it is very challenging to incorporate extra-linguistic information into pre-trained language models.
Label Errors in BANKING77 (2022)

We investigate potential label errors present in the popular BANKING77 dataset and the associated negative impacts on intent classification methods. Motivated by our own negative results when constructing an intent classifier, we applied two automated approaches to identify potential label errors in the dataset. We found that over 1,400 (14{\%}) of the 10,003 training utterances may have been incorrectly labelled. In a simple experiment, we found that by removing the utterances with potential errors, our intent classifier saw an increase of 4.5{\%} and 8{\%} for the F1-Score and Adjusted Rand Index, respectively, in supervised and unsupervised classification. This paper serves as a warning of the potential of noisy labels in popular NLP datasets. Further study is needed to fully identify the breadth and depth of label errors in BANKING77 and other datasets.
Pathologies of Pre-trained Language Models in Few-shot Fine-tuning (2022)

Although adapting pre-trained language models with few examples has shown promising performance on text classification, there is a lack of understanding of where the performance gain comes from. In this work, we propose to answer this question by interpreting the adaptation behavior using post-hoc explanations from model predictions. By modeling feature statistics of explanations, we discover that (1) without fine-tuning, pre-trained models (e.g. BERT and RoBERTa) show strong prediction bias across labels; (2) although few-shot fine-tuning can mitigate the prediction bias and demonstrate promising prediction performance, our analysis shows models gain performance improvement by capturing non-task-related features (e.g. stop words) or shallow data patterns (e.g. lexical overlaps). These observations alert that pursuing model performance with fewer examples may incur pathological prediction behavior, which requires further sanity check on model predictions and careful design in model evaluations in few-shot fine-tuning.
An Empirical study to understand the Compositional Prowess of Neural Dialog Models (2022)

In this work, we examine the problems associated with neural dialog models under the common theme of compositionality. Specifically, we investigate three manifestations of compositionality: (1) Productivity, (2) Substitutivity, and (3) Systematicity. These manifestations shed light on the generalization, syntactic robustness, and semantic capabilities of neural dialog models. We design probing experiments by perturbing the training data to study the above phenomenon. We make informative observations based on automated metrics and hope that this work increases research interest in understanding the capacity of these models.
Combining Extraction and Generation for Constructing Belief-Consequence Causal Links (2022)

In this paper, we introduce and justify a new task{---}causal link extraction based on beliefs{---}and do a qualitative analysis of the ability of a large language model{---}InstructGPT-3{---}to generate implicit consequences of beliefs. With the language model-generated consequences being promising, but not consistent, we propose directions of future work, including data collection, explicit consequence extraction using rule-based and language modeling-based approaches, and using explicitly stated consequences of beliefs to fine-tune or prompt the language model to produce outputs suitable for the task.
Replicability under Near-Perfect Conditions -- A Case-Study from Automatic Summarization (2022)

Replication of research results has become more and more important in Natural Language Processing. Nevertheless, we still rely on results reported in the literature for comparison. Additionally, elements of an experimental setup are not always completely reported. This includes, but is not limited to reporting specific parameters used or omitting an implementational detail. In our experiment based on two frequently used data sets from the domain of automatic summarization and the seemingly full disclosure of research artefacts, we examine how well results reported are replicable and what elements influence the success or failure of replication. Our results indicate that publishing research artifacts is far from sufficient, that that publishing all relevant parameters in all possible detail is cruicial.
BPE beyond Word Boundary: How NOT to use Multi Word Expressions in Neural Machine Translation (2022)

BPE tokenization merges characters into longer tokens by finding frequently occurring \textbf{contiguous} patterns \textbf{within} the word boundary. An intuitive relaxation would be to extend a BPE vocabulary with multi-word expressions (MWEs): bigrams ($in\_a$), trigrams ($out\_of\_the$), and skip-grams ($he . his$). In the context of Neural Machine Translation (NMT), we replace the least frequent subword/whole-word tokens with the most frequent MWEs. We find that these modifications to BPE end up hurting the model, resulting in a net drop of BLEU and chrF scores across two language pairs. We observe that naively extending BPE beyond word boundaries results in incoherent tokens which are themselves better represented as individual words. Moreover, we find that Pointwise Mutual Information (PMI) instead of frequency finds better MWEs (e.g., $New\_York$, $Statue\_of\_Liberty$, $neither . nor$) which consistently improves translation performance.We release all code at \url{https://github.com/pegasus-lynx/mwe-bpe}.
Pre-trained language models evaluating themselves - A comparative study (2022)

Evaluating generated text received new attention with the introduction of model-based metrics in recent years. These new metrics have a higher correlation with human judgments and seemingly overcome many issues of previous n-gram based metrics from the symbolic age. In this work, we examine the recently introduced metrics BERTScore, BLEURT, NUBIA, MoverScore, and Mark-Evaluate (Petersen). We investigate their sensitivity to different types of semantic deterioration (part of speech drop and negation), word order perturbations, word drop, and the common problem of repetition. No metric showed appropriate behaviour for negation, and further none of them was overall sensitive to the other issues mentioned above.
\textquotedblleftSlow Service\textquotedblright \ensuremath\nrightarrow \textquotedblleftGreat Food\textquotedblright: Enhancing Content Preservation in Unsupervised Text Style Transfer (2022)
Arabic Image Captioning using Pre-training of Deep Bidirectional Transformers (2022)
Evaluating Legal Accuracy of Neural Generators on the Generation of Criminal Court Dockets Description (2022)
Generating Coherent and Informative Descriptions for Groups of Visual Objects and Categories: A Simple Decoding Approach (2022)
Dealing with hallucination and omission in neural Natural Language Generation: A use case on meteorology. (2022)
Comparing informativeness of an NLG chatbot vs graphical app in diet-information domain (2022)
Keyword Provision Question Generation for Facilitating Educational Reading Comprehension Preparation (2022)
Look and Answer the Question: On the Role of Vision in Embodied Question Answering (2022)
LAFT: Cross-lingual Transfer for Text Generation by Language-Agnostic Finetuning (2022)
Are Current Decoding Strategies Capable of Facing the Challenges of Visual Dialogue? (2022)
Analogy Generation by Prompting Large Language Models: A Case Study of InstructGPT (2022)
BLAB Reporter: Automated journalism covering the Blue Amazon (2022)

This demo paper introduces BLAB reporter, a robot-journalist system covering the Brazilian Blue Amazon. The application is based on a pipeline architecture for Natural Language Generation, which offers daily reports, news summaries and curious facts in Brazilian Portuguese. By collecting, storing and analysing structured data from publicly available sources, the robot-journalist uses domain knowledge to generate, validate and publish texts in Twitter. Code and corpus are publicly available.
Generating Quizzes to Support Training on Quality Management and Assurance in Space Science and Engineering (2022)

Quality management and assurance is key for space agencies to guarantee the success of space missions, which are high-risk and extremely costly. In this paper, we present a system to generate quizzes, a common resource to evaluate the effectiveness of training sessions, from documents about quality assurance procedures in the Space domain. Our system leverages state of the art auto-regressive models like T5 and BART to generate questions, and a RoBERTa model to extract answers for such questions, thus verifying their suitability.
Automated Ad Creative Generation (2022)

Ad creatives are ads served to users on a webpage, app, or other digital environments. The demand for compelling ad creatives surges drastically with the ever-increasing popularity of digital marketing. The two most essential elements of (display) ad creatives are the advertising message, such as headlines and description texts, and the visual component, such as images and videos. Traditionally, ad creatives are composed by professional copywriters and creative designers. The process requires significant human effort, limiting the scalability and efficiency of digital ad campaigns. This work introduces AUTOCREATIVE, a novel system to automatically generate ad creatives relying on natural language generation and computer vision techniques. The system generates multiple ad copies (ad headlines/description texts) using a sequence-to-sequence model and selects images most suitable to the generated ad copies based on heuristic-based visual appeal metrics and a text-image retrieval pipeline.
THEaiTRobot: An Interactive Tool for Generating Theatre Play Scripts (2022)

We present a free online demo of THEaiTRobot, an open-source bilingual tool for interactively generating theatre play scripts, in two versions. THEaiTRobot 1.0 uses the GPT-2 language model with minimal adjustments. THEaiTRobot 2.0 uses two models created by fine-tuning GPT-2 on purposefully collected and processed datasets and several other components, generating play scripts in a hierarchical fashion (title $\rightarrow$ synopsis $\rightarrow$ script). The underlying tool is used in the THEaiTRE project to generate scripts for plays, which are then performed on stage by a professional theatre.
The Second Automatic Minuting (AutoMin) Challenge: Generating and Evaluating Minutes from Multi-Party Meetings (2022)

We would host the AutoMin generation chal- lenge at INLG 2023 as a follow-up of the first AutoMin shared task at Interspeech 2021. Our shared task primarily concerns the automated generation of meeting minutes from multi-party meeting transcripts. In our first venture, we ob- served the difficulty of the task and highlighted a number of open problems for the community to discuss, attempt, and solve. Hence, we invite the Natural Language Generation (NLG) com- munity to take part in the second iteration of AutoMin. Like the first, the second AutoMin will feature both English and Czech meetings and the core task of summarizing the manually- revised transcripts into bulleted minutes. A new challenge we are introducing this year is to devise efficient metrics for evaluating the quality of minutes. We will also host an optional track to generate minutes for European parliamentary sessions. We carefully curated the datasets for the above tasks. Our ELITR Minuting Corpus has been recently accepted to LREC 2022 and publicly released. We are already preparing a new test set for evaluating the new shared tasks. We hope to carry forward the learning from the first AutoMin and instigate more community attention and interest in this timely yet chal- lenging problem. INLG, the premier forum for the NLG community, would be an appropriate venue to discuss the challenges and future of Automatic Minuting. The main objective of the AutoMin GenChal at INLG 2023 would be to come up with efficient methods to auto- matically generate meeting minutes and design evaluation metrics to measure the quality of the minutes.
The Cross-lingual Conversation Summarization Challenge (2022)

We propose the shared task of cross-lingual conversation summarization, ConvSumX Challenge, opening new avenues for researchers to investigate solutions that integrate conversation summarization and machine translation. This task can be particularly useful due to the emergence of online meetings and conferences. We use a new benchmark, covering 2 real-world scenarios and 3 language directions, including a low-resource language, for evaluation. We hope that ConvSumX can motivate research to go beyond English and break the barrier for non-English speakers to benefit from recent advances of conversation summarization.
HinglishEval Generation Challenge on Quality Estimation of Synthetic Code-Mixed Text: Overview and Results (2022)

We hosted a shared task to investigate the factors influencing the quality of the code- mixed text generation systems. The teams experimented with two systems that gener- ate synthetic code-mixed Hinglish sentences. They also experimented with human ratings that evaluate the generation quality of the two systems. The first-of-its-kind, proposed sub- tasks, (i) quality rating prediction and (ii) an- notators' disagreement prediction of the syn- thetic Hinglish dataset made the shared task quite popular among the multilingual research community. A total of 46 participants com- prising 23 teams from 18 institutions reg- istered for this shared task. The detailed description of the task and the leaderboard is available at \url{https://codalab.lisn.upsaclay.fr/competitions/1688}.
PreCogIIITH at HinglishEval : Leveraging Code-Mixing Metrics \& Language Model Embeddings To Estimate Code-Mix Quality (2022)

Code-Mixing is a phenomenon of mixing two or more languages in a speech event and is prevalent in multilingual societies. Given the low-resource nature of Code-Mixing, machine generation of code-mixed text is a prevalent approach for data augmentation. However, evaluating the quality of such machine gen- erated code-mixed text is an open problem. In our submission to HinglishEval, a shared- task collocated with INLG2022, we attempt to build models factors that impact the quality of synthetically generated code-mix text by pre- dicting ratings for code-mix quality. Hingli- shEval Shared Task consists of two sub-tasks - a) Quality rating prediction); b) Disagree- ment prediction. We leverage popular code- mixed metrics and embeddings of multilin- gual large language models (MLLMs) as fea- tures, and train task specific MLP regression models. Our approach could not beat the baseline results. However, for Subtask-A our team ranked a close second on F-1 and Co- hen`s Kappa Score measures and first for Mean Squared Error measure. For Subtask-B our ap- proach ranked third for F1 score, and first for Mean Squared Error measure. Code of our submission can be accessed here.
niksss at HinglishEval: Language-agnostic BERT-based Contextual Embeddings with Catboost for Quality Evaluation of the Low-Resource Synthetically Generated Code-Mixed Hinglish Text (2022)

This paper describes the system description for the HinglishEval challenge at INLG 2022. The goal of this task was to investigate the factors influencing the quality of the code- mixed text generation system. The task was divided into two subtasks, quality rating prediction and annotators' disagreement prediction of the synthetic Hinglish dataset. We attempted to solve these tasks using sentence-level embeddings, which are obtained from mean pooling the contextualized word embeddings for all input tokens in our text. We experimented with various classifiers on top of the embeddings produced for respective tasks. Our best-performing system ranked 1st on subtask B and 3rd on subtask A. We make our code available here: \url{https://github.com/nikhilbyte/Hinglish-qEval}
BITS Pilani at HinglishEval: Quality Evaluation for Code-Mixed Hinglish Text Using Transformers (2022)

Code-Mixed text data consists of sentences having words or phrases from more than one language. Most multi-lingual communities worldwide communicate using multiple languages, with English usually one of them. Hinglish is a Code-Mixed text composed of Hindi and English but written in Roman script. This paper aims to determine the factors influencing the quality of Code-Mixed text data generated by the system. For the HinglishEval task, the proposed model uses multilingual BERT to find the similarity between synthetically generated and human-generated sentences to predict the quality of synthetically generated Hinglish sentences.