ymoslem/acl-abstracts huggingface dataset:
Structured Extraction of Terms and Conditions from German and English Online Shops (2022)

The automated analysis of Terms and Conditions has gained attention in recent years, mainly due to its relevance to consumer protection. Well-structured data sets are the base for every analysis. While content extraction, in general, is a well-researched field and many open source libraries are available, our evaluation shows, that existing solutions cannot extract Terms and Conditions in sufficient quality, mainly because of their special structure. In this paper, we present an approach to extract the content and hierarchy of Terms and Conditions from German and English online shops. Our evaluation shows, that the approach outperforms the current state of the art. A python implementation of the approach is made available under an open license.
\textquotedblleftDoes it come in black?\textquotedblright CLIP-like models are zero-shot recommenders (2022)

Product discovery is a crucial component for online shopping. However, item-to-item recommendations today do not allow users to explore changes along selected dimensions: given a query item, can a model suggest something similar but in a different color? We consider item recommendations of the comparative nature (e.g. {\textquotedblleft}something darker{\textquotedblright}) and show how CLIP-based models can support this use case in a zero-shot manner. Leveraging a large model built for fashion, we introduce GradREC and its industry potential, and offer a first rounded assessment of its strength and weaknesses.
Clause Topic Classification in German and English Standard Form Contracts (2022)

So-called standard form contracts, i.e. contracts that are drafted unilaterally by one party, like terms and conditions of online shops or terms of services of social networks, are cornerstones of our modern economy. Their processing is, therefore, of significant practical value. Often, the sheer size of these contracts allows the drafting party to hide unfavourable terms from the other party. In this paper, we compare different approaches for automatically classifying the topics of clauses in standard form contracts, based on a data-set of more than 6,000 clauses from more than 170 contracts, which we collected from German and English online shops and annotated based on a taxonomy of clause topics, that we developed together with legal experts. We will show that, in our comparison of seven approaches, from simple keyword matching to transformer language models, BERT performed best with an F1-score of up to 0.91, however much simpler and computationally cheaper models like logistic regression also achieved similarly good results of up to 0.87.
Investigating the Generative Approach for Question Answering in E-Commerce (2022)

Many e-commerce websites provide Product-related Question Answering (PQA) platform where potential customers can ask questions related to a product, and other consumers can post an answer to that question based on their experience. Recently, there has been a growing interest in providing automated responses to product questions. In this paper, we investigate the suitability of the generative approach for PQA. We use state-of-the-art generative models proposed by Deng et al.(2020) and Lu et al.(2020) for this purpose. On closer examination, we find several drawbacks in this approach: (1) input reviews are not always utilized significantly for answer generation, (2) the performance of the models is abysmal while answering the numerical questions, (3) many of the generated answers contain phrases like {\textquotedblleft}I do not know{\textquotedblright} which are taken from the reference answer in training data, and these answers do not convey any information to the customer. Although these approaches achieve a high ROUGE score, it does not reflect upon these shortcomings of the generated answers. We hope that our analysis will lead to more rigorous PQA approaches, and future research will focus on addressing these shortcomings in PQA.
Utilizing Cross-Modal Contrastive Learning to Improve Item Categorization BERT Model (2022)

Item categorization (IC) is a core natural language processing (NLP) task in e-commerce. As a special text classification task, fine-tuning pre-trained models, e.g., BERT, has become a mainstream solution. To improve IC performance further, other product metadata, e.g., product images, have been used. Although multimodal IC (MIC) systems show higher performance, expanding from processing text to more resource-demanding images brings large engineering impacts and hinders the deployment of such dual-input MIC systems. In this paper, we proposed a new way of using product images to improve text-only IC model: leveraging cross-modal signals between products' titles and associated images to adapt BERT models in a self-supervised learning (SSL) way. Our experiments on the three genres in the public Amazon product dataset show that the proposed method generates improved prediction accuracy and macro-F1 values than simply using the original BERT. Moreover, the proposed method is able to keep using existing text-only IC inference implementation and shows a resource advantage than the deployment of a dual-input MIC system.
Towards Generalizeable Semantic Product Search by Text Similarity Pre-training on Search Click Logs (2022)

Recently, semantic search has been successfully applied to E-commerce product search and the learned semantic space for query and product encoding are expected to generalize well to unseen queries or products. Yet, whether generalization can conveniently emerge has not been thoroughly studied in the domain thus far. In this paper, we examine several general-domain and domain-specific pre-trained Roberta variants and discover that general-domain fine-tuning does not really help generalization which aligns with the discovery of prior art, yet proper domain-specific fine-tuning with clickstream data can lead to better model generalization, based on a bucketed analysis of a manually annotated query-product relevance data.
Can Pretrained Language Models Generate Persuasive, Faithful, and Informative Ad Text for Product Descriptions? (2022)

For any e-commerce service, persuasive, faithful, and informative product descriptions can attract shoppers and improve sales. While not all sellers are capable of providing such interesting descriptions, a language generation system can be a source of such descriptions at scale, and potentially assist sellers to improve their product descriptions. Most previous work has addressed this task based on statistical approaches (Wang et al., 2017), limited attributes such as titles (Chen et al., 2019; Chan et al., 2020), and focused on only one product type (Wang et al., 2017; Munigala et al., 2018; Hong et al., 2021). In this paper, we jointly train image features and 10 text attributes across 23 diverse product types, with two different target text types with different writing styles: bullet points and paragraph descriptions. Our findings suggest that multimodal training with modern pretrained language models can generate fluent and persuasive advertisements, but are less faithful and informative, especially out of domain.
A Simple Baseline for Domain Adaptation in End to End ASR Systems Using Synthetic Data (2022)

Automatic Speech Recognition(ASR) has been dominated by deep learning-based end-to-end speech recognition models. These approaches require large amounts of labeled data in the form of audio-text pairs. Moreover, these models are more susceptible to domain shift as compared to traditional models. It is common practice to train generic ASR models and then adapt them to target domains using comparatively smaller data sets. We consider a more extreme case of domain adaptation where text-only corpus is available. In this work, we propose a simple baseline technique for domain adaptation in end-to-end speech recognition models. We convert the text-only corpus to audio data using single speaker Text to Speech (TTS) engine. The parallel data in the target domain is then used to fine-tune the final dense layer of generic ASR models. We show that single speaker synthetic TTS data coupled with final dense layer only fine-tuning provides reasonable improvements in word error rates. We use text data from address and e-commerce search domains to show the effectiveness of our low-cost baseline approach on CTC and attention-based models.
Lot or Not: Identifying Multi-Quantity Offerings in E-Commerce (2022)

The term \textit{lot} in is defined to mean an offering that contains a collection of multiple identical items for sale. In a large online marketplace, lot offerings play an important role, allowing buyers and sellers to set price levels to optimally balance supply and demand needs. In spite of their central role, platforms often struggle to identify lot offerings, since explicit lot status identification is frequently not provided by sellers. The ability to identify lot offerings plays a key role in many fundamental tasks, from matching offerings to catalog products, through ranking search results, to providing effective pricing guidance. In this work, we seek to determine the lot status (and lot size) of each offering, in order to facilitate an improved buyer experience, while reducing the friction for sellers posting new offerings. We demonstrate experimentally the ability to accurately classify offerings as lots and predict their lot size using only the offer title, by adapting state-of-the-art natural language techniques to the lot identification problem.
Multi-Domain Adaptation in Neural Machine Translation with Dynamic Sampling Strategies (2022)

Building effective Neural Machine Translation models often implies accommodating diverse sets of heterogeneous data so as to optimize performance for the domain(s) of interest. Such multi-source / multi-domain adaptation problems are typically approached through instance selection or reweighting strategies, based on a static assessment of the relevance of training instances with respect to the task at hand. In this paper, we study dynamic data selection strategies that are able to automatically re-evaluate the usefulness of data samples and to evolve a data selection policy in the course of training. Based on the results of multiple experiments, we show that such methods constitute a generic framework to automatically and effectively handle a variety of real-world situations, from multi-source domain adaptation to multi-domain learning and unsupervised domain adaptation.
The use of online translators by students not enrolled in a professional translation program: beyond copying and pasting for a professional use (2022)

In this paper, we discuss a use of machine translation (MT) that has been quite overlooked up to now, namely by students not enrolled in a professional translation program. A number of studies have reported massive use of free online translators (OTs), and it seems important to uncover such users' abilities and difficulties when using MT output, whether to improve their understanding, writing, or translation skills. We report here a study on students enrolled in a French {\textquoteleft}applied languages program' (where students study two languages, as well as law, economics, and management). The aim was to uncover how they use OTs, as well as their (in)ability to identify and correct MT errors. Obtained through two online surveys and several tests conducted with students from 2020 to 2022, our results show an unsurprising widespread use of OTs for many different tasks, but also some specific difficulties in identifying MT errors, in particular in relation to target language fluency.
Comparing and combining tagging with different decoding algorithms for back-translation in NMT: learnings from a low resource scenario (2022)

Back-translation is a well established approach to improve the performance of Neural Machine Translation (NMT) systems when large monolingual corpora of the target language and domain are available. Recently, diverse approaches have been proposed to get better automatic evaluation results of NMT models using back-translation, including the use of sampling instead of beam search as decoding algorithm for creating the synthetic corpus. Alternatively, it has been proposed to append a tag to the back-translated corpus for helping the NMT system to distinguish the synthetic bilingual corpus from the authentic one. However, not all the combinations of the previous approaches have been tested, and thus it is not clear which is the best approach for developing a given NMT system. In this work, we empirically compare and combine existing techniques for back-translation in a real low resource setting: the translation of clinical notes from Basque into Spanish. Apart from automatically evaluating the MT systems, we ask bilingual healthcare workers to perform a human evaluation, and analyze the different synthetic corpora by measuring their lexical diversity (LD). For reproducibility and generalizability, we repeat our experiments for German to English translation using public data. The results suggest that in lower resource scenarios tagging only helps when using sampling for decoding, in contradiction with the previous literature using bigger corpora from the news domain. When fine-tuning with a few thousand bilingual in-domain sentences, one of our proposed method (tagged restricted sampling) obtains the best results both in terms of automatic and human evaluation. We will publish the code upon acceptance.
Passing Parser Uncertainty to the Transformer: Labeled Dependency Distributions for Neural Machine Translation (2022)

Existing syntax-enriched neural machine translation (NMT) models work either with the single most-likely unlabeled parse or the set of n-best unlabeled parses coming out of an external parser. Passing a single or n-best parses to the NMT model risks propagating parse errors. Furthermore, unlabeled parses represent only syntactic groupings without their linguistically relevant categories. In this paper we explore the question: Does passing both parser uncertainty and labeled syntactic knowledge to the Transformer improve its translation performance? This paper contributes a novel method for infusing the whole labeled dependency distributions (LDD) of the source sentence`s dependency forest into the self-attention mechanism of the encoder of the Transformer. A range of experimental results on three language pairs demonstrate that the proposed approach outperforms both the vanilla Transformer as well as the single best-parse Transformer model across several evaluation metrics.
How well do real-time machine translation apps perform in practice? Insights from a literature review (2022)

Although more and more professionals are using real-time machine translation during dialogues with interlocutors who speak a different language, the performance of real-time MT apps has received only limited attention in the academic literature. This study summarizes the findings of prior studies (N = 34) reporting an evaluation of one or more real-time MT apps in a professional setting. Our findings show that real-time MT apps are often tested in realistic circumstances and that users are more frequently employed as judges of performance than professional translators. Furthermore, most studies report overall positive results with regard to performance, particularly when apps are tested in real-life situations.
Searching for COMETINHO: The Little Metric That Could (2022)

In recent years, several neural fine-tuned machine translation evaluation metrics such as COMET and BLEURT have been proposed. These metrics achieve much higher correlations with human judgments than lexical overlap metrics at the cost of computational efficiency and simplicity, limiting their applications to scenarios in which one has to score thousands of translation hypothesis (e.g. scoring multiple systems or Minimum Bayes Risk decoding). In this paper, we explore optimization techniques, pruning, and knowledge distillation to create more compact and faster COMET versions. Our results show that just by optimizing the code through the use of caching and length batching we can reduce inference time between 39{\%} and 65{\%} when scoring multiple systems. Also, we show that pruning COMET can lead to a 21{\%} model reduction without affecting the model`s accuracy beyond 0.01 Kendall tau correlation. Furthermore, we present DISTIL-COMET a lightweight distilled version that is 80{\%} smaller and 2.128x faster while attaining a performance close to the original model and above strong baselines such as BERTSCORE and PRISM.
Studying Post-Editese in a Professional Context: A Pilot Study (2022)

The past few years have seen the multiplication of studies on post-editese, following the massive adoption of post-editing in professional translation workflows. These studies mainly rely on the comparison of post-edited machine translation and human translation on artificial parallel corpora. By contrast, we investigate here post-editese on comparable corpora of authentic translation jobs for the language direction English into French. We explore commonly used scores and also proposes the use of a novel metric. Our analysis shows that post-edited machine translation is not only lexically poorer than human translation, but also less dense and less varied in terms of translation solutions. It also tends to be more prolific than human translation for our language direction. Finally, our study highlights some of the challenges of working with comparable corpora in post-editese research.
Diformer: Directional Transformer for Neural Machine Translation (2022)

Autoregressive (AR) and Non-autoregressive (NAR) models have their own superiority on the performance and latency, combining them into one model may take advantage of both. Current combination frameworks focus more on the integration of multiple decoding paradigms with a unified generative model, e.g. Masked Language Model. However, the generalization can be harmful on the performance due to the gap between training objective and inference. In this paper, we aim to close the gap by preserving the original objective of AR and NAR under a unified framework. Specifically, we propose the Directional Transformer (Diformer) by jointly modelling AR and NAR into three generation directions (left-to-right, right-to-left and straight) with a newly introduced direction variable, which works by controlling the prediction of each token to have specific dependencies under that direction. The unification achieved by direction successfully preserves the original dependency assumption used in AR and NAR, retaining both generalization and performance. Experiments on 4 WMT benchmarks demonstrate that Diformer outperforms current united-modelling works with more than 1.5 BLEU points for both AR and NAR decoding, and is also competitive to the state-of-the-art independent AR and NAR models.
Multilingual Neural Machine Translation With the Right Amount of Sharing (2022)

Large multilingual Transformer-based machine translation models have had a pivotal role in making translation systems available for hundreds of languages with good zero-shot translation performance. One such example is the universal model with shared encoder-decoder architecture. Additionally, jointly trained language-specific encoder-decoder systems have been proposed for multilingual neural machine translation (NMT) models. This work investigates various knowledge-sharing approaches on the encoder side while keeping the decoder language- or language-group-specific. We propose a novel approach, where we use universal, language-group-specific and language-specific modules to solve the shortcomings of both the universal models and models with language-specific encoders-decoders. Experiments on a multilingual dataset set up to model real-world scenarios, including zero-shot and low-resource translation, show that our proposed models achieve higher translation quality compared to purely universal and language-specific approaches.
Literary translation as a three-stage process: machine translation, post-editing and revision (2022)

This study focuses on English-Dutch literary translations that were created in a professional environment using an MT-enhanced workflow consisting of a three-stage process of automatic translation followed by post-editing and (mainly) monolingual revision. We compare the three successive versions of the target texts. We used different automatic metrics to measure the (dis)similarity between the consecutive versions and analyzed the linguistic characteristics of the three translation variants. Additionally, on a subset of 200 segments, we manually annotated all errors in the machine translation output and classified the different editing actions that were carried out. The results show that more editing occurred during revision than during post-editing and that the types of editing actions were different.
On the Interaction of Regularization Factors in Low-resource Neural Machine Translation (2022)

We explore the roles and interactions of the hyper-parameters governing regularization, and propose a range of values applicable to low-resource neural machine translation. We demonstrate that default or recommended values for high-resource settings are not optimal for low-resource ones, and that more aggressive regularization is needed when resources are scarce, in proportion to their scarcity. We explain our observations by the generalization abilities of sharp vs. flat basins in the loss landscape of a neural network. Results for four regularization factors corroborate our claim: batch size, learning rate, dropout rate, and gradient clipping. Moreover, we show that optimal results are obtained when using several of these factors, and that our findings generalize across datasets of different sizes and languages.
Controlling Extra-Textual Attributes about Dialogue Participants: A Case Study of English-to-Polish Neural Machine Translation (2022)

Unlike English, morphologically rich languages can reveal characteristics of speakers or their conversational partners, such as gender and number, via pronouns, morphological endings of words and syntax. When translating from English to such languages, a machine translation model needs to opt for a certain interpretation of textual context, which may lead to serious translation errors if extra-textual information is unavailable. We investigate this challenge in the English-to-Polish language direction. We focus on the underresearched problem of utilising external metadata in automatic translation of TV dialogue, proposing a case study where a wide range of approaches for controlling attributes in translation is employed in a multi-attribute scenario. The best model achieves an improvement of +5.81 chrF++/+6.03 BLEU, with other models achieving competitive performance. We additionally contribute a novel attribute-annotated dataset of Polish TV dialogue and a morphological analysis script used to evaluate attribute control in models.
Auxiliary Subword Segmentations as Related Languages for Low Resource Multilingual Translation (2022)

We propose a novel technique that combines alternative subword tokenizations of a single source-target language pair that allows us to leverage multilingual neural translation training methods. These alternate segmentations function like related languages in multilingual translation. Overall this improves translation accuracy for low-resource languages and produces translations that are lexically diverse and morphologically rich. We also introduce a cross-teaching technique which yields further improvements in translation accuracy and cross-lingual transfer between high- and low-resource language pairs. Compared to other strong multilingual baselines, our approach yields average gains of +1.7 BLEU across the four low-resource datasets from the multilingual TED-talks dataset. Our technique does not require additional training data and is a drop-in improvement for any existing neural translation system.