pwc-archive/papers-with-abstracts huggingface dataset:
The HW-TSC’s Speech to Speech Translation System for IWSLT 2022 Evaluation
Authors: Jiaxin Guo, Yinglu Li, Minghan Wang, Xiaosong Qiao, Yuxia Wang, Hengchao Shang, Chang Su, Yimeng Chen, Min Zhang, Shimin Tao, Hao Yang, Ying Qin
Date: 2022-05-01 00:00:00
Proceeding: IWSLT (ACL) 2022 5
Tasks: Machine Translation, Reranking, Speech-to-Speech Translation, Translation

The paper presents the HW-TSC’s pipeline and results of Offline Speech to Speech Translation for IWSLT 2022. We design a cascade system consisted of an ASR model, machine translation model and TTS model to convert the speech from one language into another language(en-de). For the ASR part, we find that better performance can be obtained by ensembling multiple heterogeneous ASR models and performing reranking on beam candidates. And we find that the combination of context-aware reranking strategy and MT model fine-tuned on the in-domain dataset is helpful to improve the performance. Because it can mitigate the problem that the inconsistency in transcripts caused by the lack of context. Finally, we use VITS model provided officially to reproduce audio files from the translation hypothesis.
CMU’s IWSLT 2022 Dialect Speech Translation System
Authors: Brian Yan, Patrick Fernandes, Siddharth Dalmia, Jiatong Shi, Yifan Peng, Dan Berrebbi, Xinyi Wang, Graham Neubig, Shinji Watanabe
Date: 2022-05-01 00:00:00
Proceeding: IWSLT (ACL) 2022 5
Tasks: Decoder, Knowledge Distillation, Machine Translation, speech-recognition, Speech Recognition, Translation, Triplet

This paper describes CMU’s submissions to the IWSLT 2022 dialect speech translation (ST) shared task for translating Tunisian-Arabic speech to English text. We use additional paired Modern Standard Arabic data (MSA) to directly improve the speech recognition (ASR) and machine translation (MT) components of our cascaded systems. We also augment the paired ASR data with pseudo translations via sequence-level knowledge distillation from an MT model and use these artificial triplet ST data to improve our end-to-end (E2E) systems. Our E2E models are based on the Multi-Decoder architecture with searchable hidden intermediates. We extend the Multi-Decoder by orienting the speech encoder towards the target language by applying ST supervision as hierarchical connectionist temporal classification (CTC) multi-task. During inference, we apply joint decoding of the ST CTC and ST autoregressive decoder branches of our modified Multi-Decoder. Finally, we apply ROVER voting, posterior combination, and minimum bayes-risk decoding with combined N-best lists to ensemble our various cascaded and E2E systems. Our best systems reached 20.8 and 19.5 BLEU on test2 (blind) and test1 respectively. Without any additional MSA data, we reached 20.4 and 19.2 on the same test sets.
JHU IWSLT 2022 Dialect Speech Translation System Description
Authors: Jinyi Yang, Amir Hussein, Matthew Wiesner, Sanjeev Khudanpur
Date: 2022-05-01 00:00:00
Proceeding: IWSLT (ACL) 2022 5
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Machine Translation, speech-recognition, Speech Recognition, Text Normalization, Translation

This paper details the Johns Hopkins speech translation (ST) system used in the IWLST2022 dialect speech translation task. Our system uses a cascade of automatic speech recognition (ASR) and machine translation (MT). We use a Conformer model for ASR systems and a Transformer model for machine translation. Surprisingly, we found that while using additional ASR training data resulted in only a negligible change in performance as measured by BLEU or word error rate (WER), aggressive text normalization improved BLEU more significantly. We also describe an approach, similar to back-translation, for improving performance using synthetic dialectal source text produced from source sentences in mismatched dialects.
Improving Machine Translation Formality Control with Weakly-Labelled Data Augmentation and Post Editing Strategies
Authors: Daniel Zhang, Jiang Yu, Pragati Verma, Ashwinkumar Ganesan, Sarah Campbell
Date: 2022-05-01 00:00:00
Proceeding: IWSLT (ACL) 2022 5
Tasks: Data Augmentation, Machine Translation

This paper describes Amazon Alexa AI’s implementation for the IWSLT 2022 shared task on formality control. We focus on the unconstrained and supervised task for en→hi (Hindi) and en→ja (Japanese) pairs where very limited formality annotated data is available. We propose three simple yet effective post editing strategies namely, T-V conversion, utilizing a verb conjugator and seq2seq models in order to rewrite the translated phrases into formal or informal language. Considering nuances for formality and informality in different languages, our analysis shows that a language-specific post editing strategy achieves the best performance. To address the unique challenge of limited formality annotations, we further develop a formality classifier to perform weakly labelled data augmentation which automatically generates synthetic formality labels from large parallel corpus. Empirical results on the IWSLT formality testset have shown that proposed system achieved significant improvements in terms of formality accuracy while retaining BLEU score on-par with baseline.
HW-TSC’s Participation in the IWSLT 2022 Isometric Spoken Language Translation
Authors: Zongyao Li, Jiaxin Guo, Daimeng Wei, Hengchao Shang, Minghan Wang, Ting Zhu, Zhanglin Wu, Zhengzhe Yu, Xiaoyu Chen, Lizhi Lei, Hao Yang, Ying Qin
Date: 2022-05-01 00:00:00
Proceeding: IWSLT (ACL) 2022 5
Tasks: Decoder, Translation

This paper presents our submissions to the IWSLT 2022 Isometric Spoken Language Translation task. We participate in all three language pairs (English-German, English-French, English-Spanish) under the constrained setting, and submit an English-German result under the unconstrained setting. We use the standard Transformer model as the baseline and obtain the best performance via one of its variants that shares the decoder input and output embedding. We perform detailed pre-processing and filtering on the provided bilingual data. Several strategies are used to train our models, such as Multilingual Translation, Back Translation, Forward Translation, R-Drop, Average Checkpoint, and Ensemble. We investigate three methods for biasing the output length: i) conditioning the output to a given target-source length-ratio class; ii) enriching the transformer positional embedding with length information and iii) length control decoding for non-autoregressive translation etc. Our submissions achieve 30.7, 41.6 and 36.7 BLEU respectively on the tst-COMMON test sets for English-German, English-French, English-Spanish tasks and 100% comply with the length requirements.
AppTek’s Submission to the IWSLT 2022 Isometric Spoken Language Translation Task
Authors: Patrick Wilken, Evgeny Matusov
Date: 2022-05-01 00:00:00
Proceeding: IWSLT (ACL) 2022 5
Tasks: Sentence, Translation

To participate in the Isometric Spoken Language Translation Task of the IWSLT 2022 evaluation, constrained condition, AppTek developed neural Transformer-based systems for English-to-German with various mechanisms of length control, ranging from source-side and target-side pseudo-tokens to encoding of remaining length in characters that replaces positional encoding. We further increased translation length compliance by sentence-level selection of length-compliant hypotheses from different system variants, as well as rescoring of N-best candidates from a single system. Length-compliant back-translated and forward-translated synthetic data, as well as other parallel data variants derived from the original MuST-C training corpus were important for a good quality/desired length trade-off. Our experimental results show that length compliance levels above 90% can be reached while minimizing losses in MT quality as measured in BERT and BLEU scores.
Hierarchical Multi-task learning framework for Isometric-Speech Language Translation
Authors: Aakash Bhatnagar, Nidhir Bhavsar, Muskaan Singh, Petr Motlicek
Date: 2022-05-01 00:00:00
Proceeding: IWSLT (ACL) 2022 5
Tasks: Machine Translation, Multi-Task Learning, Translation

This paper presents our submission for the shared task on isometric neural machine translation in International Conference on Spoken Language Translation (IWSLT). There are numerous state-of-art models for translation problems. However, these models lack any length constraint to produce short or long outputs from the source text. In this paper, we propose a hierarchical approach to generate isometric translation on MUST-C dataset, we achieve a BERTscore of 0.85, a length ratio of 1.087, a BLEU score of 42.3, and a length range of 51.03%. On the blind dataset provided by the task organizers, we obtain a BERTscore of 0.80, a length ratio of 1.10 and a length range of 47.5%. We have made our code public here https://github.com/aakash0017/Machine-Translation-ISWLT
Interactive Latent Knowledge Selection for E-Commerce Product Copywriting Generation
Authors: Zeming Wang, Yanyan Zou, Yuejian Fang, Hongshen Chen, Mian Ma, Zhuoye Ding, Bo Long
Date: 2022-05-01 00:00:00
Proceeding: ECNLP (ACL) 2022 5
Tasks: Attribute

As the multi-modal e-commerce is thriving, high-quality advertising product copywriting has gain more attentions, which plays a crucial role in the e-commerce recommender, advertising and even search platforms.The advertising product copywriting is able to enhance the user experience by highlighting the product’s characteristics with textual descriptions and thus to improve the likelihood of user click and purchase. Automatically generating product copywriting has attracted noticeable interests from both academic and industrial communities, where existing solutions merely make use of a product’s title and attribute information to generate its corresponding description.However, in addition to the product title and attributes, we observe that there are various auxiliary descriptions created by the shoppers or marketers in the e-commerce platforms (namely human knowledge), which contains valuable information for product copywriting generation, yet always accompanying lots of noises.In this work, we propose a novel solution to automatically generating product copywriting that involves all the title, attributes and denoised auxiliary knowledge.To be specific, we design an end-to-end generation framework equipped with two variational autoencoders that works interactively to select informative human knowledge and generate diverse copywriting.
Leveraging Seq2seq Language Generation for Multi-level Product Issue Identification
Authors: Yang Liu, Varnith Chordia, Hua Li, Siavash Fazeli Dehkordy, Yifei Sun, Vincent Gao, Na Zhang
Date: 2022-05-01 00:00:00
Proceeding: ECNLP (ACL) 2022 5
Tasks: Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Text Generation, Transfer Learning

In a leading e-commerce business, we receive hundreds of millions of customer feedback from different text communication channels such as product reviews. The feedback can contain rich information regarding customers’ dissatisfaction in the quality of goods and services. To harness such information to better serve customers, in this paper, we created a machine learning approach to automatically identify product issues and uncover root causes from the customer feedback text. We identify issues at two levels: coarse grained (L-Coarse) and fine grained (L-Granular). We formulate this multi-level product issue identification problem as a seq2seq language generation problem. Specifically, we utilize transformer-based seq2seq models due to their versatility and strong transfer-learning capability. We demonstrate that our approach is label efficient and outperforms the traditional approach such as multi-class multi-label classification formulation. Based on human evaluation, our fine-tuned model achieves 82.1% and 95.4% human-level performance for L-Coarse and L-Granular issue identification, respectively. Furthermore, our experiments illustrate that the model can generalize to identify unseen L-Granular issues.
Data Quality Estimation Framework for Faster Tax Code Classification
Authors: Ravi Kondadadi, Allen Williams, Nicolas Nicolov
Date: 2022-05-01 00:00:00
Proceeding: ECNLP (ACL) 2022 5
Tasks: Attribute, Attribute Value Extraction, Classification, Code Classification, Question Answering

This paper describes a novel framework to estimate the data quality of a collection of product descriptions to identify required relevant information for accurate product listing classification for tax-code assignment. Our Data Quality Estimation (DQE) framework consists of a Question Answering (QA) based attribute value extraction model to identify missing attributes and a classification model to identify bad quality records. We show that our framework can accurately predict the quality of product descriptions. In addition to identifying low-quality product listings, our framework can also generate a detailed report at a category level showing missing product information resulting in a better customer experience.
CML: A Contrastive Meta Learning Method to Estimate Human Label Confidence Scores and Reduce Data Collection Cost
Authors: Bo Dong, Yiyi Wang, Hanbo Sun, Yunji Wang, Alireza Hashemi, Zheng Du
Date: 2022-05-01 00:00:00
Proceeding: ECNLP (ACL) 2022 5
Tasks: Meta-Learning

Deep neural network models are especially susceptible to noise in annotated labels. In the real world, annotated data typically contains noise caused by a variety of factors such as task difficulty, annotator experience, and annotator bias. Label quality is critical for label validation tasks; however, correcting for noise by collecting more data is often costly. In this paper, we propose a contrastive meta-learning framework (CML) to address the challenges introduced by noisy annotated data, specifically in the context of natural language processing. CML combines contrastive and meta learning to improve the quality of text feature representations. Meta-learning is also used to generate confidence scores to assess label quality. We demonstrate that a model built on CML-filtered data outperforms a model built on clean data. Furthermore, we perform experiments on deidentified commercial voice assistant datasets and demonstrate that our model outperforms several SOTA approaches.
Improving Relevance Quality in Product Search using High-Precision Query-Product Semantic Similarity
Authors: Alireza Bagheri Garakani, Fan Yang, Wen-Yu Hua, Yetian Chen, Michinari Momma, Jingyuan Deng, Yan Gao, Yi Sun
Date: 2022-05-01 00:00:00
Proceeding: ECNLP (ACL) 2022 5
Tasks: Re-Ranking, Semantic Similarity, Semantic Textual Similarity

Ensuring relevance quality in product search is a critical task as it impacts the customer’s ability to find intended products in the short-term as well as the general perception and trust of the e-commerce system in the long term. In this work we leverage a high-precision cross-encoder BERT model for semantic similarity between customer query and products and survey its effectiveness for three ranking applications where offline-generated scores could be used: (1) as an offline metric for estimating relevance quality impact, (2) as a re-ranking feature covering head/torso queries, and (3) as a training objective for optimization. We present results on effectiveness of this strategy for the large e-commerce setting, which has general applicability for choice of other high-precision models and tasks in ranking.
Textual Content Moderation in C2C Marketplace
Authors: Yusuke Shido, Hsien-Chi Liu, Keisuke Umezawa
Date: 2022-05-01 00:00:00
Proceeding: ECNLP (ACL) 2022 5

Automatic monitoring systems for inappropriate user-generated messages have been found to be effective in reducing human operation costs in Consumer to Consumer (C2C) marketplace services, in which customers send messages directly to other customers.We propose a lightweight neural network that takes a conversation as input, which we deployed to a production service.Our results show that the system reduced the human operation costs to less than one-sixth compared to the conventional rule-based monitoring at Mercari.
Spelling Correction using Phonetics in E-commerce Search
Authors: Fan Yang, Alireza Bagheri Garakani, Yifei Teng, Yan Gao, Jia Liu, Jingyuan Deng, Yi Sun
Date: 2022-05-01 00:00:00
Proceeding: ECNLP (ACL) 2022 5
Tasks: Spelling Correction

In E-commerce search, spelling correction plays an important role to find desired products for customers in processing user-typed search queries. However, resolving phonetic errors is a critical but much overlooked area. The query with phonetic spelling errors tends to appear correct based on pronunciation but is nonetheless inaccurate in spelling (e.g., “bluetooth sound system” vs. “blutut sant sistam”) with numerous noisy forms and sparse occurrences. In this work, we propose a generalized spelling correction system integrating phonetics to address phonetic errors in E-commerce search without additional latency cost. Using India (IN) E-commerce market for illustration, the experiment shows that our proposed phonetic solution significantly improves the F1 score by 9%+ and recall of phonetic errors by 8%+. This phonetic spelling correction system has been deployed to production, currently serving hundreds of millions of customers.
Product Titles-to-Attributes As a Text-to-Text Task
Authors: Gilad Fuchs, Yoni Acriche
Date: 2022-05-01 00:00:00
Proceeding: ECNLP (ACL) 2022 5
Tasks: Attribute, Attribute Extraction

Online marketplaces use attribute-value pairs, such as brand, size, size type, color, etc. to help define important and relevant facts about a listing. These help buyers to curate their search results using attribute filtering and overall create a richer experience. Although their critical importance for listings’ discoverability, getting sellers to input tens of different attribute-value pairs per listing is costly and often results in missing information. This can later translate to the unnecessary removal of relevant listings from the search results when buyers are filtering by attribute values. In this paper we demonstrate using a Text-to-Text hierarchical multi-label ranking model framework to predict the most relevant attributes per listing, along with their expected values, using historic user behavioral data. This solution helps sellers by allowing them to focus on verifying information on attributes that are likely to be used by buyers, and thus, increase the expected recall for their listings. Specifically for eBay’s case we show that using this model can improve the relevancy of the attribute extraction process by 33.2% compared to the current highly-optimized production system. Apart from the empirical contribution, the highly generalized nature of the framework presented in this paper makes it relevant for many high-volume search-driven websites.
Product Answer Generation from Heterogeneous Sources: A New Benchmark and Best Practices
Authors: Xiaoyu Shen, Gianni Barlacchi, Marco del Tredici, Weiwei Cheng, Bill Byrne, Adrià Gispert
Date: 2022-05-01 00:00:00
Proceeding: ECNLP (ACL) 2022 5
Tasks: Answer Generation, Data Augmentation, Evidence Selection

It is of great value to answer product questions based on heterogeneous information sources available on web product pages, e.g., semi-structured attributes, text descriptions, user-provided contents, etc. However, these sources have different structures and writing styles, which poses challenges for (1) evidence ranking, (2) source selection, and (3) answer generation. In this paper, we build a benchmark with annotations for both evidence selection and answer generation covering 6 information sources. Based on this benchmark, we conduct a comprehensive study and present a set of best practices. We show that all sources are important and contribute to answering questions. Handling all sources within one single model can produce comparable confidence scores across sources and combining multiple sources for training always helps, even for sources with totally different structures. We further propose a novel data augmentation method to iteratively create training samples for answer generation, which achieves close-to-human performance with only a few thousandannotations. Finally, we perform an in-depth error analysis of model predictions and highlight the challenges for future research.
semiPQA: A Study on Product Question Answering over Semi-structured Data
Authors: Xiaoyu Shen, Gianni Barlacchi, Marco del Tredici, Weiwei Cheng, Adrià Gispert
Date: 2022-05-01 00:00:00
Proceeding: ECNLP (ACL) 2022 5
Tasks: Attribute, Question Answering, Sentence

Product question answering (PQA) aims to automatically address customer questions to improve their online shopping experience. Current research mainly focuses on finding answers from either unstructured text, like product descriptions and user reviews, or structured knowledge bases with pre-defined schemas. Apart from the above two sources, a lot of product information is represented in a semi-structured way, e.g., key-value pairs, lists, tables, json and xml files, etc. These semi-structured data can be a valuable answer source since they are better organized than free text, while being easier to construct than structured knowledge bases. However, little attention has been paid to them. To fill in this blank, here we study how to effectively incorporate semi-structured answer sources for PQA and focus on presenting answers in a natural, fluent sentence. To this end, we present semiPQA: a dataset to benchmark PQA over semi-structured data. It contains 11,243 written questions about json-formatted data covering 320 unique attribute types. Each data point is paired with manually-annotated text that describes its contents, so that we can train a neural answer presenter to present the data in a natural way. We provide baseline results and a deep analysis on the successes and challenges of leveraging semi-structured data for PQA. In general, state-of-the-art neural models can perform remarkably well when dealing with seen attribute types. For unseen attribute types, however, a noticeable drop is observed for both answer presentation and attribute ranking.
Improving Specificity in Review Response Generation with Data-Driven Data Filtering
Authors: Tannon Kew, Martin Volk
Date: 2022-05-01 00:00:00
Proceeding: ECNLP (ACL) 2022 5
Tasks: Response Generation, Specificity, Text Generation

Responding to online customer reviews has become an essential part of successfully managing and growing a business both in e-commerce and the hospitality and tourism sectors. Recently, neural text generation methods intended to assist authors in composing responses have been shown to deliver highly fluent and natural looking texts. However, they also tend to learn a strong, undesirable bias towards generating overly generic, one-size-fits-all outputs to a wide range of inputs. While this often results in ‘safe’, high-probability responses, there are many practical settings in which greater specificity is preferable. In this work we examine the task of generating more specific responses for online reviews in the hospitality domain by identifying generic responses in the training data, filtering them and fine-tuning the generation model. We experiment with a range of data-driven filtering methods and show through automatic and human evaluation that, despite a 60% reduction in the amount of training data, filtering helps to derive models that are capable of generating more specific, useful responses.