Singapore

Most e-commerce search engines use customer behavior signals to augment lexical matching and improve search relevance. Many e-commerce companies like Amazon, Alibaba, Ebay etc. operate in multiple countries with country specific stores. However, customer behavior data is sparse in newer stores. To compensate for sparsity of behavioral data in low traffic stores, search engines often use cross-listed products in some form. However, cross-listing across stores is not uniform and in many cases itself sparse. In this paper, we develop a model to identify duplicate and near-duplicate products across stores. Such a model can be used to unify product catalogs worldwide, improve product meta-data or as in our case, use near-duplicate products across multiple to improve search relevance. To capture the product similarity hierarchy, we develop an approach that integrates retrieval and ranking tasks across multiple languages in a single step based on a novel Hierarchical Ranked Multi Similarity (HRMS) Loss that combines Multi-Similarity (MS) loss and Hierarchical Triplet Loss to learn a hierarchical metric space. Our method outperforms strong baselines in terms of catalog coverage and precision of the mappings. We also show via online A/B tests that the product mappings found by our method are successful at improving search quality in low traffic stores, measured in rate of searches with at least one click, significantly by 0.8% and improving cold start product engagement measured as new product clicks significantly by 1.72% in established stores.

EMNLP 2023

Deep Metric Learning to Hierarchically Rank - An Application in Product Retrieval

ecommerce search

metric learning

information retrieval

## Welcome to NAACL 2022!
The Annual Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL) is taking place July 10-15, 2022 as a hybrid event, in Seattle, WA and online. We are happy to welcome all of you to conference! 
 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, keynote presentations, a plenary panel on the place of linguistics and symbolic structures in NLP, and an Industrial Track panel on Careers in NLP.
 
Posters (including Findings of NAACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop and will have an oral session and a poster session.
 
The program also features 6 Tutorials and 26 Workshops.
 
 We wish you a wonderful conference!
 
[The NAACL 2022 Organizing Committee](https://2022.naacl.org/committees/organization/)

NAACL 2022

2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

technical paper

**Welcome to EMNLP 2023!**

On behalf of the EMNLP 2023 Organizing Committee, I extend a warm and heartfelt welcome to all of you to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). It is with immense pleasure and excitement that we gather here in Singapore, a vibrant hub of innovation and technological advancement.

The conference program is packed with insightful presentations, thought-provoking workshops, and engaging networking opportunities. In addition to the technical sessions, we have also planned several social events that will provide you with the opportunity to connect with your colleagues.

The conference is held in person at the Resorts World Convention Centre in Singapore, and available on-line with the help of Underline.

For those in Singapore, I hope that you will find time to explore the exciting Sentosa Island, Gardens by the Bay, Singapore Botanic Gardens and the many other attractions unique to Singapore.

Of course, we are grateful to our sponsors and partners for their generous support of EMNLP 2023, whose contributions make it possible for us to host this world-class event.

Yuji Matsumoto (RIKEN AIP) 
EMNLP 2023 General Chair

To access the **EMNLP 2023** event page on Underline, you need to register for the Conference. 
Please follow **[this link](https://2023.emnlp.org/registration/)** for more details.

EMNLP 2023 took place in Singapore from Dec 6th to Dec 10th, 2023.

Controversy is a reflection of our zeitgeist, and an important aspect to any discourse. The rise of large language models (LLMs) as conversational systems has increased public reliance on these systems for answers to their various questions. Consequently, it is crucial to systematically examine how these models respond to questions that pertaining to ongoing debates. However, few such datasets exist in providing human-annotated labels reflecting the contemporary discussions. To foster research in this area, we propose a novel construction of a controversial questions dataset, expanding upon the publicly released Quora Question Pairs Dataset. This dataset presents challenges concerning knowledge recency, safety, fairness, and bias. We evaluate different LLMs using a subset of this dataset, illuminating how they handle controversial issues and the stances they adopt. This research ultimately contributes to our understanding of LLMs' interaction with controversial issues, paving the way for improvements in their comprehension and handling of complex societal debates.

DELPHI: Data for Evaluating LLMs' Performance in Handling Controversial Issues

Document understanding tasks, in particular, Visually-rich Document Entity Retrieval (VDER), have gained significant attention in recent years thanks to their broad applications in enterprise AI. However, publicly available data have been scarce for these tasks due to strict privacy constraints and high annotation costs. To make things worse, the non-overlapping entity spaces from different datasets hinder the knowledge transfer between document types. In this paper, we propose a method to collect massive-scale and weakly labeled data from the web to benefit the training of VDER models. The collected dataset, named DocumentNet, does not depend on specific document types or entity sets, making it universally applicable to all VDER tasks. The current DocumentNet consists of 30M documents spanning nearly 400 document types organized in a four-level ontology. Experiments on a set of broadly adopted VDER tasks show significant improvements when DocumentNet is incorporated into the pre-training for both classic and few-shot learning settings. With the recent emergence of large language models (LLMs), DocumentNet provides a large data source to extend their multimodal capabilities for VDER.

DocumentNet: Bridging the Data Gap in Document Pre-training

The key challenge in the attribute-value extraction (AVE) task from e-commerce sites is the scalability to diverse attributes for a large number of products in real-world e-commerce sites. To make AVE scalable to diverse attributes, recent researchers adopted a question-answering (QA)-based approach that additionally inputs the target attribute as a query to extract its values, and confirmed its advantage over a classical approach based on named-entity recognition (NER) on real-word e-commerce datasets. In this study, we argue the scalability of the NER-based approach compared to the QA-based approach, since researchers have compared BERT-based QA-based models to only a weak BiLSTM-based NER baseline trained from scratch in terms of only accuracy on datasets designed to evaluate the QA-based approach. Experimental results using a publicly available real-word dataset revealed that, under a fair setting, BERT-based NER models rival BERT-based QA models in terms of the accuracy, and their inference is faster than the QA model that processes the same product text several times to handle multiple target attributes.

Does Named Entity Recognition Truly Not Scale Up to Real-world Product Attribute Extraction? | VIDEO

In this paper, we present DUBLIN, a pixel-based model for visual document understanding that does not rely on OCR. DUBLIN can process both images and texts in documents just by the pixels and handle diverse document types and tasks. DUBLIN is pretrained on a large corpus of document images with novel tasks that enhance its visual and linguistic abilities. We evaluate DUBLIN on various benchmarks and show that it achieves state-of-the-art performance on extractive tasks such as DocVQA, InfoVQA, AI2D, OCR-VQA, RefExp, and CORD, as well as strong performance on abstraction datasets such as VisualMRC and text captioning. Our model demonstrates the potential of OCR-free document processing and opens new avenues for applications and research.

DUBLIN: Visual Document Understanding By Language-Image Network

In human-computer conversations, extracting entities such as names, street addresses and email addresses from speech is a challenging task. In this paper, we study the impact of fine-tuning pre-trained speech encoders on extracting spoken entities in human-readable form directly from speech without the need for text transcription. We illustrate that such a direct approach optimizes the encoder to transcribe only the entity relevant portions of speech ignoring the superfluous portions such as carrier phrases, or spell name entities. In the context of dialog from an enterprise virtual agent, we demonstrate that the 1-step approach outperforms the typical 2-step approach which first generates lexical transcriptions followed by text-based entity extraction for identifying spoken entities.

E2E Spoken Entity Extraction for Virtual Agents | VIDEO

We introduce EELBERT, an approach for compression of transformer-based models (e.g., BERT), with minimal impact on the accuracy of downstream tasks. This is achieved by replacing the input embedding layer of the model with dynamic, i.e. on-the-fly, embedding computations. Since the input embedding layer occupies a large portion of the model size, especially for the smaller BERT variants, replacing this layer with an embedding computation function helps us reduce the model size significantly. Empirical evaluation on the GLUE benchmark shows that our BERT variants (EELBERT) suffer minimal regression compared to the traditional BERT models. Through this approach, we are able to develop our smallest model UNO-EELBERT, which achieves a GLUE score within 4% of fully trained BERT-tiny, while being 15x smaller (1.2 MB) in size.

EELBERT: Tiny Models through Dynamic Embeddings | VIDEO

Large Language Model (LLM) has gained popularity and achieved remarkable results in open-domain tasks, but its performance in real industrial domain-specific scenarios is average due to its lack of specific domain knowledge. This issue has attracted widespread attention, but there are few relevant benchmarks available. In this paper, we provide a benchmark Question Answering (QA) dataset named MSQA, centered around Microsoft products and IT technical problems encountered by customers. This dataset contains industry cloud-specific QA knowledge, an area not extensively covered in general LLMs, making it well-suited for evaluating methods aiming to enhance LLMs' domain-specific capabilities. In addition, we propose a new model interaction paradigm that can empower LLM to achieve better performance on domain-specific tasks where it is not proficient. Extensive experiments demonstrate that the approach following our method outperforms the commonly used LLM with retrieval methods. We make our source code and sample data available at: https://aka.ms/Microsoft_QA.

Empower Large Language Model to Perform Better on Industrial Domain-Specific Question Answering

Extreme multi-label text classification is a prevalent task in industry, but it frequently encounters challenges in terms of machine learning perspectives, including model limitations, data scarcity, and time-consuming evaluation. This paper aims to mitigate these issues by introducing novel approaches. Firstly, we propose a label ranking model as an alternative to the conventional SciBERT-based classification model, enabling efficient handling of large-scale labels and accommodating new labels. Secondly, we present an active learning-based pipeline that addresses the data scarcity of new labels during the update of a classification system. Finally, we introduce ChatGPT to assist with model evaluation. Our experiments demonstrate the effectiveness of these techniques in enhancing the extreme multi-label text classification task.

Enhancing Extreme Multi-Label Text Classification: Addressing Challenges in Model, Data, and Evaluation

Recent research has investigated the use of generative language models to produce regular expressions with semantic-based approaches. However, these approaches have shown shortcomings in practical applications, particularly in terms of functional correctness, which refers to the ability to reproduce the intended function inputs by the user. To address this issue, we present a novel method called Unit-Test Driven Reinforcement Learning (UTD-RL). Our approach differs from previous methods by taking into account the crucial aspect of functional correctness and transforming it into a differentiable gradient feedback using policy gradient techniques. In which functional correctness can be evaluated through Unit Tests, a testing method that ensures regular expressions meets its design and performs as intended. Experiments conducted on three public datasets demonstrate the effectiveness of the proposed method in generating regular expressions. This method has been employed in a regulatory scenario where regular expressions can be utilized to ensure that all online content is free from non-compliant elements, thereby significantly reducing the workload of relevant personnel.

Enhancing Language Model with Unit Test Techniques for Efficient Regular Expression Generation

The text prediction (TP) workflow calls a Large Language Model (LLM), almost, after every character to get subsequent sequence of characters, till user accepts a suggestion. The confidence score of the prediction is commonly used for filtering the results to ensure that only correct predictions are shown to user. As LLMs require massive amounts of computation and storage, such an approach incurs network and high execution cost. So, we propose a Model gatekeeper (GK) to stop the LLM calls that will result in incorrect predictions at client application level itself. This way a GK can save cost of model inference and improve user experience by not showing the incorrect predictions. We demonstrate that use of a model gatekeeper saved approx 46.6% of COGS for TP, at the cost of approx 4.5% loss in character saving. Use of GK also improved the efficiency (suggestion rate) of TP model by 73%.

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DELPHI: Data for Evaluating LLMs' Performance in Handling Controversial Issues

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Augmenting Training Data for Massive Semantic Matching Models in Low-Traffic E-commerce Stores

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Next from EMNLP 2023

DELPHI: Data for Evaluating LLMs' Performance in Handling Controversial Issues

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Augmenting Training Data for Massive Semantic Matching Models in Low-Traffic E-commerce Stores

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