ymoslem/acl-abstracts huggingface dataset:
KD-Boost: Boosting Real-Time Semantic Matching in E-commerce with Knowledge Distillation (2023)

Real-time semantic matching is vital to web and product search. Transformer-based models have shown to be highly effective at encoding queries into an embedding space where semantically similar entities (queries or results) are in close proximity. However, the computational complexity of large transformer models limits their utilization for real-time matching. In this paper, we propose KD-Boost, a novel knowledge distillation algorithm designed for real-time semantic matching. KD-Boost trains low latency accurate student models by leveraging soft labels from a teacher model as well as ground truth via pairwise query-product and query-query signal derived from direct audits, user behavior, and taxonomy-based data using custom loss functions. Experiments on internal and external e-commerce datasets demonstrate an improvement of 2-3{\%} ROC-AUC compared to training student models directly, outperforming teacher and SOTA knowledge distillation benchmarks. Simulated online A/B tests using KD-Boost for automated Query Reformulation (QR) indicate a 6.31{\%} increase in query-to-query matching, 2.76{\%} increase in product coverage, and a 2.19{\%} improvement in relevance.
Multi-teacher Distillation for Multilingual Spelling Correction (2023)

Accurate spelling correction is a critical step in modern search interfaces, especially in an era of mobile devices and speech-to-text interfaces. For services that are deployed around the world, this poses a significant challenge for multilingual NLP: spelling errors need to be caught and corrected in all languages, and even in queries that use multiple languages. In this paper, we tackle this challenge using multi-teacher distillation. On our approach, a monolingual teacher model is trained for each language/locale, and these individual models are distilled into a single multilingual student model intended to serve all languages/locales. In experiments using open-source data as well as customer data from a worldwide search service, we show that this leads to highly effective spelling correction models that can meet the tight latency requirements of deployed services.
Does Named Entity Recognition Truly Not Scale Up to Real-world Product Attribute Extraction? (2023)

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.
Investigating Table-to-Text Generation Capabilities of Large Language Models in Real-World Information Seeking Scenarios (2023)

Tabular data is prevalent across various industries, necessitating significant time and effort for users to understand and manipulate for their information-seeking purposes. The advancements in large language models (LLMs) have shown enormous potential to improve user efficiency. However, the adoption of LLMs in real-world applications for table information seeking remains underexplored. In this paper, we investigate the table-to-text capabilities of different LLMs using four datasets within two real-world information seeking scenarios. These include the LogicNLG and our newly-constructed LoTNLG datasets for data insight generation, along with the FeTaQA and our newly-constructed F2WTQ datasets for query-based generation. We structure our investigation around three research questions, evaluating the performance of LLMs in table-to-text generation, automated evaluation, and feedback generation, respectively. Experimental results indicate that the current high-performing LLM, specifically GPT-4, can effectively serve as a table-to-text generator, evaluator, and feedback generator, facilitating users' information seeking purposes in real-world scenarios. However, a significant performance gap still exists between other open-sourced LLMs (e.g., Vicuna and LLaMA-2) and GPT-4 models. Our data and code are publicly available at https://github.com/yale-nlp/LLM-T2T.
TMID: A Comprehensive Real-world Dataset for Trademark Infringement Detection in E-Commerce (2023)

Annually, e-commerce platforms incur substantial financial losses due to trademark infringements, making it crucial to identify and mitigate potential legal risks tied to merchant information registered to the platforms. However, the absence of high-quality datasets hampers research in this area. To address this gap, our study introduces TMID, a novel dataset to detect trademark infringement in merchant registrations. This is a real-world dataset sourced directly from Alipay, one of the world`s largest e-commerce and digital payment platforms. As infringement detection is a legal reasoning task requiring an understanding of the contexts and legal rules, we offer a thorough collection of legal rules and merchant and trademark-related contextual information with annotations from legal experts. We ensure the data quality by performing an extensive statistical analysis. Furthermore, we conduct an empirical study on this dataset to highlight its value and the key challenges. Through this study, we aim to contribute valuable resources to advance research into legal compliance related to trademark infringement within the e-commerce sphere.
Joint Dialogue Topic Segmentation and Categorization: A Case Study on Clinical Spoken Conversations (2023)

Utilizing natural language processing techniques in clinical conversations is effective to improve the efficiency of health management workflows for medical staff and patients. Dialogue segmentation and topic categorization are two fundamental steps for processing verbose spoken conversations and highlighting informative spans for downstream tasks. However, in practical use cases, due to the variety of segmentation granularity and topic definition, and the lack of diverse annotated corpora, no generic models are readily applicable for domain-specific applications. In this work, we introduce and adopt a joint model for dialogue segmentation and topic categorization, and conduct a case study on healthcare follow-up calls for diabetes management; we provide insights from both data and model perspectives toward performance and robustness.
AdapterDistillation: Non-Destructive Task Composition with Knowledge Distillation (2023)

Leveraging knowledge from multiple tasks through introducing a small number of task specific parameters into each transformer layer, also known as adapters, receives much attention recently. However, adding an extra fusion layer to implement knowledge composition not only increases the inference time but also is non-scalable for some applications. To avoid these issues, we propose a two-stage knowledge distillation algorithm called AdapterDistillation. In the first stage, we extract task specific knowledge by using local data to train a student adapter. In the second stage, we distill the knowledge from the existing teacher adapters into the student adapter to help its inference. Extensive experiments on frequently asked question retrieval in task-oriented dialog systems validate the efficiency of AdapterDistillation. We show that AdapterDistillation outperforms existing algorithms in terms of accuracy, resource consumption and inference time.
PROMINET: Prototype-based Multi-View Network for Interpretable Email Response Prediction (2023)

Email is a widely used tool for business communication, and email marketing has emerged as a cost-effective strategy for enterprises. While previous studies have examined factors affecting email marketing performance, limited research has focused on understanding email response behavior by considering email content and metadata. This study proposes a Prototype-based Multi-view Network (PROMINET) that incorporates semantic and structural information from email data. By utilizing prototype learning, the PROMINET model generates latent exemplars, enabling interpretable email response prediction. The model maps learned semantic and structural exemplars to observed samples in the training data at different levels of granularity, such as document, sentence, or phrase. The approach is evaluated on two real-world email datasets: the Enron corpus and an in-house Email Marketing corpus. Experimental results demonstrate that the PROMINET model outperforms baseline models, achieving a {\textasciitilde}3{\%} improvement in F1 score on both datasets. Additionally, the model provides interpretability through prototypes at different granularity levels while maintaining comparable performance to non-interpretable models. The learned prototypes also show potential for generating suggestions to enhance email text editing and improve the likelihood of effective email responses. This research contributes to enhancing sender-receiver communication and customer engagement in email interactions.
Retrieval-Enhanced Dual Encoder Training for Product Matching (2023)

Product matching is the task of matching a seller-listed item to an appropriate product. It is a critical task for an e-commerce platform, and the approach needs to be efficient to run in a large-scale setting. A dual encoder approach has been a common practice for product matching recently, due to its high performance and computation efficiency. In this paper, we propose a two-stage training for the dual encoder model. Stage 1 trained a dual encoder to identify the more informative training data. Stage 2 then train on the more informative data to get a better dual encoder model. This technique is a learned approach for building training data. We evaluate the retrieval-enhanced training on two different datasets: a publicly available Large-Scale Product Matching dataset and a real-world e-commerce dataset containing 47 million products. Experiment results show that our approach improved by 2{\%} F1 on the public dataset and 9{\%} F1 on the real-world e-commerce dataset.
WordArt Designer: User-Driven Artistic Typography Synthesis using Large Language Models (2023)

This paper introduces WordArt Designer, a user-driven framework for artistic typography synthesis, relying on the Large Language Model (LLM). The system incorporates four key modules: the LLM Engine, SemTypo, StyTypo, and TexTypo modules. 1) The LLM Engine, empowered by the LLM (e.g. GPT-3.5), interprets user inputs and generates actionable prompts for the other modules, thereby transforming abstract concepts into tangible designs. 2) The SemTypo module optimizes font designs using semantic concepts, striking a balance between artistic transformation and readability. 3) Building on the semantic layout provided by the SemTypo module, the StyTypo module creates smooth, refined images. 4) The TexTypo module further enhances the design`s aesthetics through texture rendering, enabling the generation of inventive textured fonts. Notably, WordArt Designer highlights the fusion of generative AI with artistic typography. Experience its capabilities on ModelScope: https://www.modelscope.cn/studios/WordArt/WordArt.
Lattice Path Edit Distance: A Romanization-aware Edit Distance for Extracting Misspelling-Correction Pairs from Japanese Search Query Logs (2023)

Edit distance has been successfully used to extract training data, i.e., misspelling-correction pairs, of spelling correction models from search query logs in languages including English. However, the success does not readily apply to Japanese, where misspellings are often dissimilar to correct spellings due to the romanization-based input methods. To address this problem, we introduce lattice path edit distance, which utilizes romanization lattices to efficiently consider all possible romanized forms of input strings. Empirical experiments using Japanese search query logs demonstrated that the lattice path edit distance outperformed baseline methods including the standard edit distance combined with an existing transliterator and morphological analyzer. A training data collection pipeline that uses the lattice path edit distance has been deployed in production at our search engine for over a year.
Learning Multilingual Sentence Representations with Cross-lingual Consistency Regularization (2023)

Multilingual sentence representations are the foundation for similarity-based bitext mining, which is crucial for scaling multilingual neural machine translation (NMT) system to more languages. In this paper, we introduce MuSR: a one-for-all Multilingual Sentence Representation model that supports 223 languages. Leveraging billions of English-centric parallel corpora, we train a multilingual Transformer encoder, coupled with an auxiliary Transformer decoder, by adopting a multilingual NMT framework with CrossConST, a cross-lingual consistency regularization technique proposed in Gao et al. (2023). Experimental results on multilingual similarity search and bitext mining tasks show the effectiveness of our approach. Specifically, MuSR achieves superior performance over LASER3 (Heffernan et al., 2022) which consists of 148 independent multilingual sentence encoders.
Unveiling Identity Biases in Toxicity Detection : A Game-Focused Dataset and Reactivity Analysis Approach (2023)

Identity biases arise commonly from annotated datasets, can be propagated in language models and can cause further harm to marginal groups. Existing bias benchmarking datasets are mainly focused on gender or racial biases and are made to pinpoint which class the model is biased towards. They also are not designed for the gaming industry, a concern for models built for toxicity detection in videogames' chat. We propose a dataset and a method to highlight oversensitive terms using reactivity analysis and the model`s performance. We test our dataset against ToxBuster, a language model developed by Ubisoft fine-tuned for toxicity detection on multiplayer videogame`s written chat, and Perspective API. We find that these toxicity models often automatically tag terms related to a community`s identity as toxic, which prevents members of already marginalized groups to make their presence known or have a mature / normal conversation. Through this process, we have generated an interesting list of terms that trigger the models to varying degrees, along with insights on establishing a baseline through human annotations.
ORANGE: Text-video Retrieval via Watch-time-aware Heterogeneous Graph Contrastive Learning (2023)

With the explosive growth of short-video data on industrial video-sharing platforms such as TikTok and YouTube, text-video retrieval techniques have become increasingly important. Most existing works for text-video retrieval focus on designing informative representation learning methods and delicate matching mechanisms, which leverage the content information of queries and videos themselves (i.e., textual information of queries and multimodal information of videos). However, real-world scenarios often involve brief, ambiguous queries and low-quality videos, making content-based retrieval less effective. In order to accommodate various search requirements and enhance user satisfaction, this study introduces a novel Text-video Retrieval method via Watch-time-aware Heterogeneous Graph Contrastive Learning (termed ORANGE). This approach aims to learn informative embeddings for queries and videos by leveraging both content information and the abundant relational information present in video-search scenarios. Specifically, we first construct a heterogeneous information graph where nodes represent domain objects (e.g., query, video, tag) and edges represent rich relations among these objects. Afterwards, a meta-path-guided heterogeneous graph attention encoder with the awareness of video watch time is devised to encode various semantic aspects of query and video nodes. To train our model, we introduce a meta-path-wise contrastive learning paradigm that facilitates capturing dependencies across multiple semantic relations, thereby enhancing the obtained embeddings. Finally, when deployed online, for new queries non-existent in the constructed graph, a bert-based query encoder distilled from our ORANGE is employed. Offline experiments conducted on a real-world dataset demonstrate the effectiveness of our ORANGE. Moreover, it has been implemented in the matching stage of an industrial online video-search service, where it exhibited statistically significant improvements over the online baseline in an A/B test.
Compute-Efficient Churn Reduction for Conversational Agents (2023)

Model churn occurs when re-training a model yields different predictions despite using the same data and hyper-parameters. Churn reduction is crucial for industry conversational systems where users expect consistent results for the same queries. In this setting, compute resources are often limited due to latency requirements during serving and overall time constraints during re-training. To address this issue, we propose a compute-efficient method that mitigates churn without requiring extra resources for training or inference. Our approach involves a lightweight data pre-processing step that pairs semantic parses based on their {\textquotedblleft}function call signature{\textquotedblright} and encourages similarity through an additional loss based on Jensen-Shannon Divergence. We validate the effectiveness of our method in three scenarios: academic (+3.93 percent improvement on average in a churn reduction metric), simulated noisy data (+8.09), and industry (+5.28) settings.
Empower Large Language Model to Perform Better on Industrial Domain-Specific Question Answering (2023)

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.
Enhancing Extreme Multi-Label Text Classification: Addressing Challenges in Model, Data, and Evaluation (2023)

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.
Query-aware Multi-modal based Ranking Relevance in Video Search (2023)

Relevance ranking system plays a crucial role in video search on streaming platforms. Most relevance ranking methods focus on text modality, incapable of fully exploiting cross-modal cues present in video. Recent multi-modal models have demonstrated promise in various vision-language tasks but provide limited help for downstream query-video relevance tasks due to the discrepency between relevance ranking-agnostic pre-training objectives and the real video search scenarios that demand comprehensive relevance modeling. To address these challenges, we propose a QUery-Aware pre-training model with multi-modaLITY (QUALITY) that incorporates hard-mined query information as alignment targets and utilizes video tag information for guidance. QUALITY is integrated into our relevance ranking model, which leverages multi-modal knowledge and improves ranking optimization method based on ordinal regression. Extensive experiments show our proposed model significantly enhances video search performance.
Coordinated Replay Sample Selection for Continual Federated Learning (2023)

Continual Federated Learning (CFL) combines Federated Learning (FL), the decentralized learning of a central model on a number of client devices that may not communicate their data, and Continual Learning (CL), the learning of a model from a continual stream of data without keeping the entire history. In CL, the main challenge is forgetting what was learned from past data. While replay-based algorithms that keep a small pool of past training data are effective to reduce forgetting, only simple replay sample selection strategies have been applied to CFL in prior work, and no previous work has explored coordination among clients for better sample selection. To bridge this gap, we adapt a replay sample selection objective based on loss gradient diversity to CFL and propose a new relaxation-based selection of samples to optimize the objective. Next, we propose a practical algorithm to coordinate gradient-based replay sample selection across clients without communicating private data. We benchmark our coordinated and uncoordinated replay sample selection algorithms against random sampling-based baselines with language models trained on a large scale de-identified real-world text dataset. We show that gradient-based sample selection methods both boost performance and reduce forgetting compared to random sampling methods, with our coordination method showing gains early in the low replay size regime (when the budget for storing past data is small).
Building Real-World Meeting Summarization Systems using Large Language Models: A Practical Perspective (2023)

This paper studies how to effectively build meeting summarization systems for real-world usage using large language models (LLMs). For this purpose, we conduct an extensive evaluation and comparison of various closed-source and open-source LLMs, namely, GPT-4, GPT-3.5, PaLM-2, and LLaMA-2. Our findings reveal that most closed-source LLMs are generally better in terms of performance. However, much smaller open-source models like LLaMA-2 (7B and 13B) could still achieve performance comparable to the large closed-source models even in zero-shot scenarios. Considering the privacy concerns of closed-source models for only being accessible via API, alongside the high cost associated with using fine-tuned versions of the closed-source models, the opensource models that can achieve competitive performance are more advantageous for industrial use. Balancing performance with associated costs and privacy concerns, the LLaMA-2-7B model looks more promising for industrial usage. In sum, this paper offers practical insights on using LLMs for real-world business meeting summarization, shedding light on the trade-offs between performance and cost.