Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track

Yunyao Li, Angeliki Lazaridou (Editors)

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Abu Dhabi, UAE
Association for Computational Linguistics
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Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track
Yunyao Li | Angeliki Lazaridou

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Unsupervised Term Extraction for Highly Technical Domains
Francesco Fusco | Peter Staar | Diego Antognini

Term extraction is an information extraction task at the root of knowledge discovery platforms. Developing term extractors that are able to generalize across very diverse and potentially highly technical domains is challenging, as annotations for domains requiring in-depth expertise are scarce and expensive to obtain. In this paper, we describe the term extraction subsystem of a commercial knowledge discovery platform that targets highly technical fields such as pharma, medical, and material science. To be able to generalize across domains, we introduce a fully unsupervised annotator (UA). It extracts terms by combining novel morphological signals from sub-word tokenization with term-to-topic and intra-term similarity metrics, computed using general-domain pre-trained sentence-encoders. The annotator is used to implement a weakly-supervised setup, where transformer-models are fine-tuned (or pre-trained) over the training data generated by running the UA over large unlabeled corpora. Our experiments demonstrate that our setup can improve the predictive performance while decreasing the inference latency on both CPUs and GPUs. Our annotators provide a very competitive baseline for all the cases where annotations are not available.

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DynaMaR: Dynamic Prompt with Mask Token Representation
Xiaodi Sun | Sunny Rajagopalan | Priyanka Nigam | Weiyi Lu | Yi Xu | Iman Keivanloo | Belinda Zeng | Trishul Chilimbi

Recent research has shown that large language models pretrained using unsupervised approaches can achieve significant performance improvement on many downstream tasks. Typically when adapting these language models to downstream tasks, like a classification or regression task, we employ a fine-tuning paradigm in which the sentence representation from the language model is input to a task-specific head; the model is then fine-tuned end-to-end. However, with the emergence of models like GPT-3, prompt-based fine-tuning has been proven to be a successful approach for few-shot tasks. Inspired by this work, we study discrete prompt technologies in practice. There are two issues that arise with the standard prompt approach. First, it can overfit on the prompt template. Second, it requires manual effort to formulate the downstream task as a language model problem. In this paper, we propose an improvement to prompt-based fine-tuning that addresses these two issues. We refer to our approach as DynaMaR – Dynamic Prompt with Mask Token Representation. Results show that DynaMaR can achieve an average improvement of 10% in few-shot settings and improvement of 3.7% in data-rich settings over the standard fine-tuning approach on four e-commerce applications.

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A Hybrid Approach to Cross-lingual Product Review Summarization
Saleh Soltan | Victor Soto | Ke Tran | Wael Hamza

We present a hybrid approach for product review summarization which consists of: (i) an unsupervised extractive step to extract the most important sentences out of all the reviews, and (ii) a supervised abstractive step to summarize the extracted sentences into a coherent short summary. This approach allows us to develop an efficient cross-lingual abstractive summarizer that can generate summaries in any language, given the extracted sentences out of thousands of reviews in a source language. In order to train and test the abstractive model, we create the Cross-lingual Amazon Reviews Summarization (CARS) dataset which provides English summaries for training, and English, French, Italian, Arabic, and Hindi summaries for testing based on selected English reviews. We show that the summaries generated by our model are as good as human written summaries in coherence, informativeness, non-redundancy, and fluency.

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Augmenting Operations Research with Auto-Formulation of Optimization Models From Problem Descriptions
Rindra Ramamonjison | Haley Li | Timothy Yu | Shiqi He | Vishnu Rengan | Amin Banitalebi-dehkordi | Zirui Zhou | Yong Zhang

We describe an augmented intelligence system for simplifying and enhancing the modeling experience for operations research. Using this system, the user receives a suggested formulation of an optimization problem based on its description. To facilitate this process, we build an intuitive user interface system that enables the users to validate and edit the suggestions. We investigate controlled generation techniques to obtain an automatic suggestion of formulation. Then, we evaluate their effectiveness with a newly created dataset of linear programming problems drawn from various application domains.

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Knowledge Distillation based Contextual Relevance Matching for E-commerce Product Search
Ziyang Liu | Chaokun Wang | Hao Feng | Lingfei Wu | Liqun Yang

Online relevance matching is an essential task of e-commerce product search to boost the utility of search engines and ensure a smooth user experience. Previous work adopts either classical relevance matching models or Transformer-style models to address it. However, they ignore the inherent bipartite graph structures that are ubiquitous in e-commerce product search logs and are too inefficient to deploy online. In this paper, we design an efficient knowledge distillation framework for e-commerce relevance matching to integrate the respective advantages of Transformer-style models and classical relevance matching models. Especially for the core student model of the framework, we propose a novel method using k-order relevance modeling. The experimental results on large-scale real-world data (the size is 6 174 million) show that the proposed method significantly improves the prediction accuracy in terms of human relevance judgment. We deploy our method to online search platform. The A/B testing results show that our method significantly improves most business metrics under price sort mode and default sort mode.

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Accelerating the Discovery of Semantic Associations from Medical Literature: Mining Relations Between Diseases and Symptoms
Alberto Purpura | Francesca Bonin | Joao Bettencourt-silva

Medical literature is a vast and constantly expanding source of information about diseases, their diagnoses and treatments. One of the ways to extract insights from this type of data is through mining association rules between such entities. However, existing solutions do not take into account the semantics of sentences from which entity co-occurrences are extracted. We propose a scalable solution for the automated discovery of semantic associations between different entities such as diseases and their symptoms. Our approach employs the UMLS semantic network and a binary relation classification model trained with distant supervision to validate and help ranking the most likely entity associations pairs extracted with frequency-based association rule mining algorithms. We evaluate the proposed system on the task of extracting disease-symptom associations from a collection of over 14M PubMed abstracts and validate our results against a publicly available known list of disease-symptom pairs.

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PENTATRON: PErsonalized coNText-Aware Transformer for Retrieval-based cOnversational uNderstanding
Niranjan Uma Naresh | Ziyan Jiang | Ankit Ankit | Sungjin Lee | Jie Hao | Xing Fan | Chenlei Guo

Conversational understanding is an integral part of modern intelligent devices. In a large fraction of the global traffic from customers using smart digital assistants, frictions in dialogues may be attributed to incorrect understanding of the entities in a customer’s query due to factors including ambiguous mentions, mispronunciation, background noise and faulty on-device signal processing. Such errors are compounded by two common deficiencies from intelligent devices namely, (1) the device not being tailored to individual customers, and (2) the device responses being unaware of the context in the conversation session. Viewing this problem via the lens of retrieval-based search engines, we build and evaluate a scalable entity correction system, PENTATRON. The system leverages a parametric transformer-based language model to learn patterns from in-session customer-device interactions coupled with a non-parametric personalized entity index to compute the correct query, which aids downstream components in reasoning about the best response. In addition to establishing baselines and demonstrating the value of personalized and context-aware systems, we use multitasking to learn the domain of the correct entity. We also investigate the utility of language model prompts. Through extensive experiments, we show a significant upward movement of the key metric (Exact Match) by up to 500.97% (relative to the baseline).

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Machine translation impact in E-commerce multilingual search
Bryan Zhang | Amita Misra

Previous work suggests that performance of cross-lingual information retrieval correlates highly with the quality of Machine Translation. However, there may be a threshold beyond which improving query translation quality yields little or no benefit to further improve the retrieval performance. This threshold may depend upon multiple factors including the source and target languages, the existing MT system quality and the search pipeline. In order to identify the benefit of improving an MT system for a given search pipeline, we investigate the sensitivity of retrieval quality to the presence of different levels of MT quality using experimental datasets collected from actual traffic. We systematically improve the performance of our MT systems quality on language pairs as measured by MT evaluation metrics including Bleu and Chrf to determine their impact on search precision metrics and extract signals that help to guide the improvement strategies. Using this information we develop techniques to compare query translations for multiple language pairs and identify the most promising language pairs to invest and improve.

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Ask-and-Verify: Span Candidate Generation and Verification for Attribute Value Extraction
Yifan Ding | Yan Liang | Nasser Zalmout | Xian Li | Christan Grant | Tim Weninger

The product attribute value extraction (AVE) task aims to capture key factual information from product profiles, and is useful for several downstream applications in e-Commerce platforms. Previous contributions usually formulate this task using sequence labeling or reading comprehension architectures. However, sequence labeling models tend to be conservative in their predictions resulting in a high false negative rate. Existing reading comprehension formulations, on the other hand, can over-generate attribute values which hinders precision. In the present work we address these limitations with a new end-to-end pipeline framework called Ask-and-Verify. Given a product and an attribute query, the Ask step detects the top-K span candidates (i.e. possible attribute values) from the product profiles, then the Verify step filters out false positive candidates. We evaluate Ask-and-Verify model on Amazon’s product pages and AliExpress public dataset, and present a comparative analysis as well as a detailed ablation study. Despite its simplicity, we show that Ask-and-Verify outperforms recent state-of-the-art models by up to 3.1% F1 absolute improvement points, while also scaling to thousands of attributes.

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Consultation Checklists: Standardising the Human Evaluation of Medical Note Generation
Aleksandar Savkov | Francesco Moramarco | Alex Papadopoulos Korfiatis | Mark Perera | Anya Belz | Ehud Reiter

Evaluating automatically generated text is generally hard due to the inherently subjective nature of many aspects of the output quality. This difficulty is compounded in automatic consultation note generation by differing opinions between medical experts both about which patient statements should be included in generated notes and about their respective importance in arriving at a diagnosis. Previous real-world evaluations of note-generation systems saw substantial disagreement between expert evaluators. In this paper we propose a protocol that aims to increase objectivity by grounding evaluations in Consultation Checklists, which are created in a preliminary step and then used as a common point of reference during quality assessment. We observed good levels of inter-annotator agreement in a first evaluation study using the protocol; further, using Consultation Checklists produced in the study as reference for automatic metrics such as ROUGE or BERTScore improves their correlation with human judgements compared to using the original human note.

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Towards Need-Based Spoken Language Understanding Model Updates: What Have We Learned?
Quynh Do | Judith Gaspers | Daniil Sorokin | Patrick Lehnen

In productionized machine learning systems, online model performance is known to deteriorate over time when there is a distributional drift between offline training and online application data. As a remedy, models are typically retrained at fixed time intervals, implying high computational and manual costs. This work aims at decreasing such costs in productionized, large-scale Spoken Language Understanding systems. In particular, we develop a need-based re-training strategy guided by an efficient drift detector and discuss the arising challenges including system complexity, overlapping model releases, observation limitation and the absence of annotated resources at runtime. We present empirical results on historical data and confirm the utility of our design decisions via an online A/B experiment.

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Knowledge Distillation Transfer Sets and their Impact on Downstream NLU Tasks
Charith Peris | Lizhen Tan | Thomas Gueudre | Turan Gojayev | Pan Wei | Gokmen Oz

Teacher-student knowledge distillation is a popular technique for compressing today’s prevailing large language models into manageable sizes that fit low-latency downstream applications. Both the teacher and the choice of transfer set used for distillation are crucial ingredients in creating a high quality student. Yet, the generic corpora used to pretrain the teacher and the corpora associated with the downstream target domain are often significantly different, which raises a natural question: should the student be distilled over the generic corpora, so as to learn from high-quality teacher predictions, or over the downstream task corpora to align with finetuning? Our study investigates this trade-off using Domain Classification (DC) and Intent Classification/Named Entity Recognition (ICNER) as downstream tasks. We distill several multilingual students from a larger multilingual LM with varying proportions of generic and task-specific datasets, and report their performance after finetuning on DC and ICNER. We observe significant improvements across tasks and test sets when only task-specific corpora is used. We also report on how the impact of adding task-specific data to the transfer set correlates with the similarity between generic and task-specific data. Our results clearly indicate that, while distillation from a generic LM benefits downstream tasks, students learn better using target domain data even if it comes at the price of noisier teacher predictions. In other words, target domain data still trumps teacher knowledge.

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Exploiting In-Domain Bilingual Corpora for Zero-Shot Transfer Learning in NLU of Intra-Sentential Code-Switching Chatbot Interactions
Maia Aguirre | Manex Serras | Laura García-sardiña | Jacobo López-fernández | Ariane Méndez | Arantza Del Pozo

Code-switching (CS) is a very common phenomenon in regions with various co-existing languages. Since CS is such a frequent habit in informal communications, both spoken and written, it also arises naturally in Human-Machine Interactions. Therefore, in order for natural language understanding (NLU) not to be degraded, CS must be taken into account when developing chatbots. The co-existence of multiple languages in a single NLU model has become feasible with multilingual language representation models such as mBERT. In this paper, the efficacy of zero-shot cross-lingual transfer learning with mBERT for NLU is evaluated on a Basque-Spanish CS chatbot corpus, comparing the performance of NLU models trained using in-domain chatbot utterances in Basque and/or Spanish without CS. The results obtained indicate that training joint multi-intent classification and entity recognition models on both languages simultaneously achieves best performance, better capturing the CS patterns.

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Calibrating Imbalanced Classifiers with Focal Loss: An Empirical Study
Cheng Wang | Jorge Balazs | György Szarvas | Patrick Ernst | Lahari Poddar | Pavel Danchenko

Imbalanced data distribution is a practical and common challenge in building production-level machine learning (ML) models in industry, where data usually exhibits long-tail distributions. For instance, in virtual AI Assistants, such as Google Assistant, Amazon Alexa and Apple Siri, the “play music” or “set timer” utterance is exposed to an order of magnitude more traffic than other skills. This can easily cause trained models to overfit to the majority classes, categories or intents, lead to model miscalibration. The uncalibrated models output unreliable (mostly overconfident) predictions, which are at high risk of affecting downstream decision-making systems. In this work, we study the calibration of production models in the industry use-case of predicting product return reason codes in customer service conversations of an online retail store; The returns reasons also exhibit class imbalance. To alleviate the resulting miscalibration in the production ML model, we streamline the model development and deployment using focal loss (CITATION).We empirically show the effectiveness of model training with focal loss in learning better calibrated models, as compared to standard cross-entropy loss. Better calibration, in turn, enables better control of the precision-recall trade-off for the models deployed in production.

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Unsupervised training data re-weighting for natural language understanding with local distribution approximation
Jose Garrido Ramas | Dieu-thu Le | Bei Chen | Manoj Kumar | Kay Rottmann

One of the major challenges of training Natural Language Understanding (NLU) production models lies in the discrepancy between the distributions of the offline training data and of the online live data, due to, e.g., biased sampling scheme, cyclic seasonality shifts, annotated training data coming from a variety of different sources, and a changing pool of users. Consequently, the model trained by the offline data is biased. We often observe this problem especially in task-oriented conversational systems, where topics of interest and the characteristics of users using the system change over time. In this paper we propose an unsupervised approach to mitigate the offline training data sampling bias in multiple NLU tasks. We show that a local distribution approximation in the pre-trained embedding space enables the estimation of importance weights for training samples guiding re-sampling for an effective bias mitigation. We illustrate our novel approach using multiple NLU datasets and show improvements obtained without additional annotation, making this a general approach for mitigating effects of sampling bias.

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Cross-Encoder Data Annotation for Bi-Encoder Based Product Matching
Justin Chiu | Keiji Shinzato

Matching a seller listed item to an appropriate product is an important step for an e-commerce platform. With the recent advancement in deep learning, there are different encoder based approaches being proposed as solution. When textual data for two products are available, cross-encoder approaches encode them jointly while bi-encoder approaches encode them separately. Since cross-encoders are computationally heavy, approaches based on bi-encoders are a common practice for this challenge. In this paper, we propose cross-encoder data annotation; a technique to annotate or refine human annotated training data for bi-encoder models using a cross-encoder model. This technique enables us to build a robust model without annotation on newly collected training data or further improve model performance on annotated training data. We evaluate the cross-encoder data annotation on the product matching task using a real-world e-commerce dataset containing 104 million products. Experimental results show that the cross-encoder data annotation improves 4% absolute accuracy when no annotation for training data is available, and 2% absolute accuracy when annotation for training data is available.

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Deploying a Retrieval based Response Model for Task Oriented Dialogues
Lahari Poddar | György Szarvas | Cheng Wang | Jorge Balazs | Pavel Danchenko | Patrick Ernst

Task-oriented dialogue systems in industry settings need to have high conversational capability, be easily adaptable to changing situations and conform to business constraints. This paper describes a 3-step procedure to develop a conversational model that satisfies these criteria and can efficiently scale to rank a large set of response candidates. First, we provide a simple algorithm to semi-automatically create a high-coverage template set from historic conversations without any annotation. Second, we propose a neural architecture that encodes the dialogue context and applicable business constraints as profile features for ranking the next turn. Third, we describe a two-stage learning strategy with self-supervised training, followed by supervised fine-tuning on limited data collected through a human-in-the-loop platform. Finally, we describe offline experiments and present results of deploying our model with human-in-the-loop to converse with live customers online.

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Tackling Temporal Questions in Natural Language Interface to Databases
Ngoc Phuoc An Vo | Octavian Popescu | Irene Manotas | Vadim Sheinin

Temporal aspect is one of the most challenging areas in Natural Language Interface to Databases (NLIDB). This paper addresses and examines how temporal questions being studied and supported by the research community at both levels: popular annotated dataset (e.g. Spider) and recent advanced models. We present a new dataset with accompanied databases supporting temporal questions in NLIDB. We experiment with two SOTA models (Picard and ValueNet) to investigate how our new dataset helps these models learn and improve performance in temporal aspect.

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Multi-Tenant Optimization For Few-Shot Task-Oriented FAQ Retrieval
Asha Vishwanathan | Rajeev Warrier | Gautham Vadakkekara Suresh | Chandra Shekhar Kandpal

Business-specific Frequently Asked Questions (FAQ) retrieval in task-oriented dialog systems poses unique challenges vis à vis community based FAQs. Each FAQ question represents an intent which is usually an umbrella term for many related user queries. We evaluate performance for such Business FAQs both with standard FAQ retrieval techniques using query-Question (q-Q) similarity and few-shot intent detection techniques. Implementing a real-world solution for FAQ retrieval in order to support multiple tenants (FAQ sets) entails optimizing speed, accuracy and cost. We propose a novel approach to scale multi-tenant FAQ applications in real-world context by contrastive fine-tuning of the last layer in sentence Bi-Encoders along with tenant-specific weight switching.

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Iterative Stratified Testing and Measurement for Automated Model Updates
Elizabeth Dekeyser | Nicholas Comment | Shermin Pei | Rajat Kumar | Shruti Rai | Fengtao Wu | Lisa Haverty | Kanna Shimizu

Automating updates to machine learning systems is an important but understudied challenge in AutoML. The high model variance of many cutting-edge deep learning architectures means that retraining a model provides no guarantee of accurate inference on all sample types. To address this concern, we present Automated Data-Shape Stratified Model Updates (ADSMU), a novel framework that relies on iterative model building coupled with data-shape stratified model testing and improvement. Using ADSMU, we observed a 26% (relative) improvement in accuracy for new model use cases on a large-scale NLU system, compared to a naive (manually) retrained baseline and current cutting-edge methods.

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SLATE: A Sequence Labeling Approach for Task Extraction from Free-form Inked Content
Apurva Gandhi | Ryan Serrao | Biyi Fang | Gilbert Antonius | Jenna Hong | Tra My Nguyen | Sheng Yi | Ehi Nosakhare | Irene Shaffer | Soundararajan Srinivasan

We present SLATE, a sequence labeling approach for extracting tasks from free-form content such as digitally handwritten (or “inked”) notes on a virtual whiteboard. Our approach allows us to create a single, low-latency model to simultaneously perform sentence segmentation and classification of these sentences into task/non-task sentences. SLATE greatly outperforms a baseline two-model (sentence segmentation followed by classification model) approach, achieving a task F1 score of 84.4%, a sentence segmentation (boundary similarity) score of 88.4% and three times lower latency compared to the baseline. Furthermore, we provide insights into tackling challenges of performing NLP on the inking domain. We release both our code and dataset for this novel task.

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Gaining Insights into Unrecognized User Utterances in Task-Oriented Dialog Systems
Ella Rabinovich | Matan Vetzler | David Boaz | Vineet Kumar | Gaurav Pandey | Ateret Anaby Tavor

The rapidly growing market demand for automatic dialogue agents capable of goal-oriented behavior has caused many tech-industry leaders to invest considerable efforts into task-oriented dialog systems. The success of these systems is highly dependent on the accuracy of their intent identification – the process of deducing the goal or meaning of the user’s request and mapping it to one of the known intents for further processing. Gaining insights into unrecognized utterances – user requests the systems fails to attribute to a known intent – is therefore a key process in continuous improvement of goal-oriented dialog systems. We present an end-to-end pipeline for processing unrecognized user utterances, deployed in a real-world, commercial task-oriented dialog system, including a specifically-tailored clustering algorithm, a novel approach to cluster representative extraction, and cluster naming. We evaluated the proposed components, demonstrating their benefits in the analysis of unrecognized user requests.

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CoCoID: Learning Contrastive Representations and Compact Clusters for Semi-Supervised Intent Discovery
Qian Cao | Deyi Xiong | Qinlong Wang | Xia Peng

Intent discovery is to mine new intents from user utterances, which are not present in the set of manually predefined intents. Previous approaches to intent discovery usually automatically cluster novel intents with prior knowledge from intent-labeled data in a semi-supervised way. In this paper, we focus on the discriminative user utterance representation learning and the compactness of the learned intent clusters. We propose a novel semi-supervised intent discovery framework CoCoID with two essential components: contrastive user utterance representation learning and intra-cluster knowledge distillation. The former attempts to detect similar and dissimilar intents from a minibatch-wise perspective. The latter regularizes the predictive distribution of the model over samples in a cluster-wise way. We conduct experiments on both real-life challenging datasets (i.e., CLINC and BANKING) that are curated to emulate the true environment of commercial/production systems and traditional datasets (i.e., StackOverflow and DBPedia) to evaluate the proposed CoCoID. Experiment results demonstrate that our model substantially outperforms state-of-the-art intent discovery models (12 baselines) by over 1.4 ACC and ARI points and 1.1 NMI points across the four datasets. Further analyses suggest that CoCoID is able to learn contrastive representations and compact clusters for intent discovery.

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Tractable & Coherent Multi-Document Summarization: Discrete Optimization of Multiple Neural Modeling Streams via Integer Linear Programming
Litton J Kurisinkel | Nancy Chen

One key challenge in multi-document summarization is the generated summary is often less coherent compared to single document summarization due to the larger heterogeneity of the input source content. In this work, we propose a generic framework to jointly consider coherence and informativeness in multi-document summarization and offers provisions to replace individual components based on the domain of source text. In particular, the framework characterizes coherence through verb transitions and entity mentions and takes advantage of syntactic parse trees and neural modeling for intra-sentential noise pruning. The framework cast the entire problem as an integer linear programming optimization problem with neural and non-neural models as linear components. We evaluate our method in the news and legal domains. The proposed approach consistently performs better than competitive baselines for both objective metrics and human evaluation.

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Grafting Pre-trained Models for Multimodal Headline Generation
Lingfeng Qiao | Chen Wu | Ye Liu | Haoyuan Peng | Di Yin | Bo Ren

Multimodal headline utilizes both video frames and transcripts to generate the natural language title of the videos. Due to a lack of large-scale, manually annotated data, the task of annotating grounded headlines for video is labor intensive and impractical. Previous researches on pre-trained language models and video-language models have achieved significant progress in related downstream tasks. However, none of them can be directly applied to multimodal headline architecture where we need both multimodal encoder and sentence decoder. A major challenge in simply gluing language model and video-language model is the modality balance, which is aimed at combining visual-language complementary abilities. In this paper, we propose a novel approach to graft the video encoder from the pre-trained video-language model on the generative pre-trained language model. We also present a consensus fusion mechanism for the integration of different components, via inter/intra modality relation. Empirically, experiments show that the grafted model achieves strong results on a brand-new dataset collected from real-world applications.

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Semi-supervised Adversarial Text Generation based on Seq2Seq models
Hieu Le | Dieu-thu Le | Verena Weber | Chris Church | Kay Rottmann | Melanie Bradford | Peter Chin

To improve deep learning models’ robustness, adversarial training has been frequently used in computer vision with satisfying results. However, adversarial perturbation on text have turned out to be more challenging due to the discrete nature of text. The generated adversarial text might not sound natural or does not preserve semantics, which is the key for real world applications where text classification is based on semantic meaning. In this paper, we describe a new way for generating adversarial samples by using pseudo-labeled in-domain text data to train a seq2seq model for adversarial generation and combine it with paraphrase detection. We showcase the benefit of our approach for a real-world Natural Language Understanding (NLU) task, which maps a user’s request to an intent. Furthermore, we experiment with gradient-based training for the NLU task and try using token importance scores to guide the adversarial text generation. We show that our approach can generate realistic and relevant adversarial samples compared to other state-of-the-art adversarial training methods. Applying adversarial training using these generated samples helps the NLU model to recover up to 70% of these types of errors and makes the model more robust, especially in the tail distribution in a large scale real world application.

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Is it out yet? Automatic Future Product Releases Extraction from Web Data
Gilad Fuchs | Ido Ben-shaul | Matan Mandelbrod

Identifying the release of new products and their predicted demand in advance is highly valuable for E-Commerce marketplaces and retailers. The information of an upcoming product release is used for inventory management, marketing campaigns and pre-order suggestions. Often, the announcement of an upcoming product release is widely available in multiple web pages such as blogs, chats or news articles. However, to the best of our knowledge, an automatic system to extract future product releases from web data has not been presented. In this work we describe an ML-powered multi-stage pipeline to automatically identify future product releases and rank their predicted demand from unstructured pages across the whole web. Our pipeline includes a novel Longformer-based model which uses a global attention mechanism guided by pre-calculated Named Entity Recognition predictions related to product releases. The model training data is based on a new corpus of 30K web pages manually annotated to identify future product releases. We made the dataset openly available at

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Automatic Scene-based Topic Channel Construction System for E-Commerce
Peng Lin | Yanyan Zou | Lingfei Wu | Mian Ma | Zhuoye Ding | Bo Long

Scene marketing that well demonstrates user interests within a certain scenario has proved effective for offline shopping. To conduct scene marketing for e-commerce platforms, this work presents a novel product form, scene-based topic channel which typically consists of a list of diverse products belonging to the same usage scenario and a topic title that describes the scenario with marketing words. As manual construction of channels is time-consuming due to billions of products as well as dynamic and diverse customers’ interests, it is necessary to leverage AI techniques to automatically construct channels for certain usage scenarios and even discover novel topics. To be specific, we first frame the channel construction task as a two-step problem, i.e., scene-based topic generation and product clustering, and propose an E-commerce Scene-based Topic Channel construction system (i.e., ESTC) to achieve automated production, consisting of scene-based topic generation model for the e-commerce domain, product clustering on the basis of topic similarity, as well as quality control based on automatic model filtering and human screening. Extensive offline experiments and online A/B test validates the effectiveness of such a novel product form as well as the proposed system. In addition, we also introduce the experience of deploying the proposed system on a real-world e-commerce recommendation platform.

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SpeechNet: Weakly Supervised, End-to-End Speech Recognition at Industrial Scale
Raphael Tang | Karun Kumar | Gefei Yang | Akshat Pandey | Yajie Mao | Vladislav Belyaev | Madhuri Emmadi | Craig Murray | Ferhan Ture | Jimmy Lin

End-to-end automatic speech recognition systems represent the state of the art, but they rely on thousands of hours of manually annotated speech for training, as well as heavyweight computation for inference. Of course, this impedes commercialization since most companies lack vast human and computational resources. In this paper, we explore training and deploying an ASR system in the label-scarce, compute-limited setting. To reduce human labor, we use a third-party ASR system as a weak supervision source, supplemented with labeling functions derived from implicit user feedback. To accelerate inference, we propose to route production-time queries across a pool of CUDA graphs of varying input lengths, the distribution of which best matches the traffic’s. Compared to our third-party ASR, we achieve a relative improvement in word-error rate of 8% and a speedup of 600%. Our system, called SpeechNet, currently serves 12 million queries per day on our voice-enabled smart television. To our knowledge, this is the first time a large-scale, Wav2vec-based deployment has been described in the academic literature.

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Controlled Language Generation for Language Learning Items
Kevin Stowe | Debanjan Ghosh | Mengxuan Zhao

This work aims to employ natural language generation (NLG) to rapidly generate items for English language learning applications: this requires both language models capable of generating fluent, high-quality English, and to control the output of the generation to match the requirements of the relevant items. We experiment with deep pretrained models for this task, developing novel methods for controlling items for factors relevant in language learning: diverse sentences for different proficiency levels and argument structure to test grammar. Human evaluation demonstrates high grammatically scores for all models (3.4 and above out of 4), and higher length (24%) and complexity (9%) over the baseline for the advanced proficiency model. Our results show that we can achieve strong performance while adding additional control to ensure diverse, tailored content for individual users.

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Improving Text-to-SQL Semantic Parsing with Fine-grained Query Understanding
Jun Wang | Patrick Ng | Alexander Hanbo Li | Jiarong Jiang | Zhiguo Wang | Bing Xiang | Ramesh Nallapati | Sudipta Sengupta

Most recent research on Text-to-SQL semantic parsing relies on either parser itself or simple heuristic based approach to understand natural language query (NLQ). When synthesizing a SQL query, there is no explicit semantic information of NLQ available to the parser which leads to undesirable generalization performance. In addition, without lexical-level fine-grained query understanding, linking between query and database can only rely on fuzzy string match which leads to suboptimal performance in real applications. In view of this, in this paper we present a general-purpose, modular neural semantic parsing framework that is based on token-level fine-grained query understanding. Our framework consists of three modules: named entity recognizer (NER), neural entity linker (NEL) and neural semantic parser (NSP). By jointly modeling query and database, NER model analyzes user intents and identifies entities in the query. NEL model links typed entities to schema and cell values in database. Parser model leverages available semantic information and linking results and synthesizes tree-structured SQL queries based on dynamically generated grammar. Experiments on SQUALL, a newly released semantic parsing dataset, show that we can achieve 56.8% execution accuracy on WikiTableQuestions (WTQ) test set, which outperforms the state-of-the-art model by 2.7%.

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Unsupervised Dense Retrieval for Scientific Articles
Dan Li | Vikrant Yadav | Zubair Afzal | George Tsatsaronis

In this work, we build a dense retrieval based semantic search engine on scientific articles from Elsevier. The major challenge is that there is no labeled data for training and testing. We apply a state-of-the-art unsupervised dense retrieval model called Generative Pseudo Labeling that generates high-quality pseudo training labels. Furthermore, since the articles are unbalanced across different domains, we select passages from multiple domains to form balanced training data. For the evaluation, we create two test sets: one manually annotated and one automatically created from the meta-information of our data. We compare the semantic search engine with the currently deployed lexical search engine on the two test sets. The results of the experiment show that the semantic search engine trained with pseudo training labels can significantly improve search performance.

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Learning Geolocations for Cold-Start and Hard-to-Resolve Addresses via Deep Metric Learning
Govind | Saurabh Sohoney

With evergrowing digital adoption in the society and increasing demand for businesses to deliver to customers doorstep, the last mile hop of transportation planning poses unique challenges in emerging geographies with unstructured addresses. One of the crucial inputs to facilitate effective planning is the task of geolocating customer addresses. Existing systems operate by aggregating historical delivery locations or by resolving/matching addresses to known buildings and campuses to vend a high-precision geolocation. However, by design they fail to cater to a significant fraction of addresses which are new in the system and have inaccurate or missing building level information. We propose a framework to resolve these addresses (referred to as hard-to-resolve henceforth) to a shallower granularity termed as neighbourhood. Specifically, we propose a weakly supervised deep metric learning model to encode the geospatial semantics in address embeddings. We present empirical evaluation on India (IN) and the United Arab Emirates (UAE) hard-to-resolve addresses to show significant improvements in learning geolocations i.e., 22% (IN) & 55% (UAE) reduction in delivery defects (where learnt geocode is Y meters away from actual location), and 43% (IN) & 90% (UAE) reduction in 50th percentile (p50) distance between learnt and actual delivery locations over the existing production system.

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Meta-learning Pathologies from Radiology Reports using Variance Aware Prototypical Networks
Arijit Sehanobish | Kawshik Kannan | Nabila Abraham | Anasuya Das | Benjamin Odry

Large pretrained Transformer-based language models like BERT and GPT have changed the landscape of Natural Language Processing (NLP). However, fine tuning such models still requires a large number of training examples for each target task, thus annotating multiple datasets and training these models on various downstream tasks becomes time consuming and expensive. In this work, we propose a simple extension of the Prototypical Networks for few-shot text classification. Our main idea is to replace the class prototypes by Gaussians and introduce a regularization term that encourages the examples to be clustered near the appropriate class centroids. Experimental results show that our method outperforms various strong baselines on 13 public and 4 internal datasets. Furthermore, we use the class distributions as a tool for detecting potential out-of-distribution (OOD) data points during deployment.

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Named Entity Recognition in Industrial Tables using Tabular Language Models
Aneta Koleva | Martin Ringsquandl | Mark Buckley | Rakeb Hasan | Volker Tresp

Specialized transformer-based models for encoding tabular data have gained interest in academia. Although tabular data is omnipresent in industry, applications of table transformers are still missing. In this paper, we study how these models can be applied to an industrial Named Entity Recognition (NER) problem where the entities are mentioned in tabular-structured spreadsheets. The highly technical nature of spreadsheets as well as the lack of labeled data present major challenges for fine-tuning transformer-based models. Therefore, we develop a dedicated table data augmentation strategy based on available domain-specific knowledge graphs. We show that this boosts performance in our low-resource scenario considerably. Further, we investigate the benefits of tabular structure as inductive bias compared to tables as linearized sequences. Our experiments confirm that a table transformer outperforms other baselines and that its tabular inductive bias is vital for convergence of transformer-based models.

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Reinforced Question Rewriting for Conversational Question Answering
Zhiyu Chen | Jie Zhao | Anjie Fang | Besnik Fetahu | Oleg Rokhlenko | Shervin Malmasi

Conversational Question Answering (CQA) aims to answer questions contained within dialogues, which are not easily interpretable without context. Developing a model to rewrite conversational questions into self-contained ones is an emerging solution in industry settings as it allows using existing single-turn QA systems to avoid training a CQA model from scratch. Previous work trains rewriting models using human rewrites as supervision. However, such objectives are disconnected with QA models and therefore more human-like rewrites do not guarantee better QA performance. In this paper we propose using QA feedback to supervise the rewriting model with reinforcement learning. Experiments show that our approach can effectively improve QA performance over baselines for both extractive and retrieval QA. Furthermore, human evaluation shows that our method can generate more accurate and detailed rewrites when compared to human annotations.

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Improving Large-Scale Conversational Assistants using Model Interpretation based Training Sample Selection
Stefan Schroedl | Manoj Kumar | Kiana Hajebi | Morteza Ziyadi | Sriram Venkatapathy | Anil Ramakrishna | Rahul Gupta | Pradeep Natarajan

This paper presents an approach to identify samples from live traffic where the customer implicitly communicated satisfaction with Alexa’s responses, by leveraging interpretations of model behavior. Such customer signals are noisy and adding a large number of samples from live traffic to training set makes re-training infeasible. Our work addresses these challenges by identifying a small number of samples that grow training set by ~0.05% while producing statistically significant improvements in both offline and online tests.

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Improving Precancerous Case Characterization via Transformer-based Ensemble Learning
Yizhen Zhong | Jiajie Xiao | Thomas Vetterli | Mahan Matin | Ellen Loo | Jimmy Lin | Richard Bourgon | Ofer Shapira

The application of natural language processing (NLP) to cancer pathology reports has been focused on detecting cancer cases, largely ignoring precancerous cases. Improving the characterization of precancerous adenomas assists in developing diagnostic tests for early cancer detection and prevention, especially for colorectal cancer (CRC). Here we developed transformer-based deep neural network NLP models to perform the CRC phenotyping, with the goal of extracting precancerous lesion attributes and distinguishing cancer and precancerous cases. We achieved 0.914 macro-F1 scores for classifying patients into negative, non-advanced adenoma, advanced adenoma and CRC. We further improved the performance to 0.923 using an ensemble of classifiers for cancer status classification and lesion size named-entity recognition (NER). Our results demonstrated the potential of using NLP to leverage real-world health record data to facilitate the development of diagnostic tests for early cancer prevention.

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Developing Prefix-Tuning Models for Hierarchical Text Classification
Lei Chen | Houwei Chou | Xiaodan Zhu

Hierarchical text classification (HTC) is a key problem and task in many industrial applications, which aims to predict labels organized in a hierarchy for given input text. For example, HTC can group the descriptions of online products into a taxonomy or organizing customer reviews into a hierarchy of categories. In real-life applications, while Pre-trained Language Models (PLMs) have dominated many NLP tasks, they face significant challenges too—the conventional fine-tuning process needs to modify and save models with a huge number of parameters. This is becoming more critical for HTC in both global and local modelling—the latter needs to learn multiple classifiers at different levels/nodes in a hierarchy. The concern will be even more serious since PLM sizes are continuing to increase in order to attain more competitive performances. Most recently, prefix tuning has become a very attractive technology by only tuning and saving a tiny set of parameters. Exploring prefix turning for HTC is hence highly desirable and has timely impact. In this paper, we investigate prefix tuning on HTC in two typical setups: local and global HTC. Our experiment shows that the prefix-tuning model only needs less than 1% of parameters and can achieve performance comparable to regular full fine-tuning. We demonstrate that using contrastive learning in learning prefix vectors can further improve HTC performance.

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PAIGE: Personalized Adaptive Interactions Graph Encoder for Query Rewriting in Dialogue Systems
Daniel Biś | Saurabh Gupta | Jie Hao | Xing Fan | Chenlei Guo

Unexpected responses or repeated clarification questions from conversational agents detract from the users’ experience with technology meant to streamline their daily tasks. To reduce these frictions, Query Rewriting (QR) techniques replace transcripts of faulty queries with alternatives that lead to responses thatsatisfy the users’ needs. Despite their successes, existing QR approaches are limited in their ability to fix queries that require considering users’ personal preferences. We improve QR by proposing Personalized Adaptive Interactions Graph Encoder (PAIGE).PAIGE is the first QR architecture that jointly models user’s affinities and query semantics end-to-end. The core idea is to represent previous user-agent interactions and world knowledge in a structured form — a heterogeneous graph — and apply message passing to propagate latent representations of users’ affinities to refine utterance embeddings.Using these embeddings, PAIGE can potentially provide different rewrites given the same query for users with different preferences. Our model, trained without any human-annotated data, improves the rewrite retrieval precision of state-of-the-art baselines by 12.5–17.5% while having nearly ten times fewer parameters.

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Fast Vocabulary Transfer for Language Model Compression
Leonidas Gee | Andrea Zugarini | Leonardo Rigutini | Paolo Torroni

Real-world business applications require a trade-off between language model performance and size. We propose a new method for model compression that relies on vocabulary transfer. We evaluate the method on various vertical domains and downstream tasks. Our results indicate that vocabulary transfer can be effectively used in combination with other compression techniques, yielding a significant reduction in model size and inference time while marginally compromising on performance.

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Multimodal Context Carryover
Prashan Wanigasekara | Nalin Gupta | Fan Yang | Emre Barut | Zeynab Raeesy | Kechen Qin | Stephen Rawls | Xinyue Liu | Chengwei Su | Spurthi Sandiri

Multi-modality support has become an integral part of creating a seamless user experience with modern voice assistants with smart displays. Users refer to images, video thumbnails, or the accompanying text descriptions on the screen through voice communication with AI powered devices. This raises the need to either augment existing commercial voice only dialogue systems with state-of-the-art multimodal components, or to introduce entirely new architectures; where the latter can lead to costly system revamps. To support the emerging visual navigation and visual product selection use cases, we propose to augment commercially deployed voice-only dialogue systems with additional multi-modal components. In this work, we present a novel yet pragmatic approach to expand an existing dialogue-based context carryover system (Chen et al., 2019a) in a voice assistant with state-of-the-art multimodal components to facilitate quick delivery of visual modality support with minimum changes. We demonstrate a 35% accuracy improvement over the existing system on an in-house multi-modal visual navigation data set.

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Distilling Multilingual Transformers into CNNs for Scalable Intent Classification
Besnik Fetahu | Akash Veeragouni | Oleg Rokhlenko | Shervin Malmasi

We describe an application of Knowledge Distillation used to distill and deploy multilingual Transformer models for voice assistants, enabling text classification for customers globally. Transformers have set new state-of-the-art results for tasks like intent classification, and multilingual models exploit cross-lingual transfer to allow serving requests across 100+ languages. However, their prohibitive inference time makes them impractical to deploy in real-world scenarios with low latency requirements, such as is the case of voice assistants. We address the problem of cross-architecture distillation of multilingual Transformers to simpler models, while maintaining multilinguality without performance degradation. Training multilingual student models has received little attention, and is our main focus. We show that a teacher-student framework, where the teacher’s unscaled activations (logits) on unlabelled data are used to supervise student model training, enables distillation of Transformers into efficient multilingual CNN models. Our student model achieves equivalent performance as the teacher, and outperforms a similar model trained on the labelled data used to train the teacher model. This approach has enabled us to accurately serve global customer requests at speed (18x improvement), scale, and low cost.

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Bringing the State-of-the-Art to Customers: A Neural Agent Assistant Framework for Customer Service Support
Stephen Obadinma | Faiza Khan Khattak | Shirley Wang | Tania Sidhorn | Elaine Lau | Sean Robertson | Jingcheng Niu | Winnie Au | Alif Munim | Karthik Raja Kalaiselvi Bhaskar

Building Agent Assistants that can help improve customer service support requires inputs from industry users and their customers, as well as knowledge about state-of-the-art Natural Language Processing (NLP) technology. We combine expertise from academia and industry to bridge the gap and build task/domain-specific Neural Agent Assistants (NAA) with three high-level components for: (1) Intent Identification, (2) Context Retrieval, and (3) Response Generation. In this paper, we outline the pipeline of the NAA’s core system and also present three case studies in which three industry partners successfully adapt the framework to find solutions to their unique challenges. Our findings suggest that a collaborative process is instrumental in spurring the development of emerging NLP models for Conversational AI tasks in industry. The full reference implementation code and results are available at

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Zero-Shot Dynamic Quantization for Transformer Inference
Yousef El-kurdi | Jerry Quinn | Avi Sil

We introduce a novel run-time method for significantly reducing the accuracy loss associated with quantizing BERT-like models to 8-bit integers. Existing methods for quantizing models either modify the training procedure, or they require an additional calibration step to adjust parameters that also requires a selected held-out dataset. Our method permits taking advantage of quantization without the need for these adjustments. We present results on several NLP tasks demonstrating the usefulness of this technique.

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Fact Checking Machine Generated Text with Dependency Trees
Alex Estes | Nikhita Vedula | Marcus Collins | Matt Cecil | Oleg Rokhlenko

Factual and logical errors made by Natural Language Generation (NLG) systems limit their applicability in many settings. We study this problem in a conversational search and recommendation setting, and observe that we can often make two simplifying assumptions in this domain: (i) there exists a body of structured knowledge we can use for verifying factuality of generated text; and (ii) the text to be factually assessed typically has a well-defined structure and style. Grounded in these assumptions, we propose a fast, unsupervised and explainable technique, DepChecker, that assesses factuality of input text based on rules derived from structured knowledge patterns and dependency relations with respect to the input text. We show that DepChecker outperforms state-of-the-art, general purpose fact-checking techniques in this special, but important case.

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Prototype-Representations for Training Data Filtering in Weakly-Supervised Information Extraction
Nasser Zalmout | Xian Li

The availability of high quality training data is still a bottleneck for the practical utilization of information extraction models, despite the breakthroughs in zero and few-shot learning techniques. This is further exacerbated for industry applications, where new tasks, domains, and specific use cases keep arising, which makes it impractical to depend on manually annotated data. Therefore, weak and distant supervision emerged as popular approaches to bootstrap training, utilizing labeling functions to guide the annotation process. Weakly-supervised annotation of training data is fast and efficient, however, it results in many irrelevant and out-of-context matches. This is a challenging problem that can degrade the performance in downstream models, or require a manual data cleaning step that can incur significant overhead. In this paper we present a prototype-based filtering approach, that can be utilized to denoise weakly supervised training data. The system is very simple, unsupervised, scalable, and requires little manual intervention, yet results in significant precision gains. We apply the technique in the task of attribute value extraction in e-commerce websites, and achieve up to 9% gain in precision for the downstream models, with a minimal drop in recall.

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CGF: Constrained Generation Framework for Query Rewriting in Conversational AI
Jie Hao | Yang Liu | Xing Fan | Saurabh Gupta | Saleh Soltan | Rakesh Chada | Pradeep Natarajan | Chenlei Guo | Gokhan Tur

In conversational AI agents, Query Rewriting (QR) plays a crucial role in reducing user frictions and satisfying their daily demands. User frictions are caused by various reasons, such as errors in the conversational AI system, users’ accent or their abridged language. In this work, we present a novel Constrained Generation Framework (CGF) for query rewriting at both global and personalized levels. It is based on the encoder-decoder framework, where the encoder takes the query and its previous dialogue turns as the input to form a context-enhanced representation, and the decoder uses constrained decoding to generate the rewrites based on the pre-defined global or personalized constrained decoding space. Extensive offline and online A/B experiments show that the proposed CGF significantly boosts the query rewriting performance.

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Entity-level Sentiment Analysis in Contact Center Telephone Conversations
Xue-yong Fu | Cheng Chen | Md Tahmid Rahman Laskar | Shayna Gardiner | Pooja Hiranandani | Shashi Bhushan Tn

Entity-level sentiment analysis predicts the sentiment about entities mentioned in a given text. It is very useful in a business context to understand user emotions towards certain entities, such as products or companies. In this paper, we demonstrate how we developed an entity-level sentiment analysis system that analyzes English telephone conversation transcripts in contact centers to provide business insight. We present two approaches, one entirely based on the transformer-based DistilBERT model, and another that uses a neural network supplemented with some heuristic rules.

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QUILL: Query Intent with Large Language Models using Retrieval Augmentation and Multi-stage Distillation
Krishna Srinivasan | Karthik Raman | Anupam Samanta | Lingrui Liao | Luca Bertelli | Michael Bendersky

Large Language Models (LLMs) have shown impressive results on a variety of text understanding tasks. Search queries though pose a unique challenge, given their short-length and lack of nuance or context. Complicated feature engineering efforts do not always lead to downstream improvements as their performance benefits may be offset by increased complexity of knowledge distillation. Thus, in this paper we make the following contributions: (1) We demonstrate that Retrieval Augmentation of queries provides LLMs with valuable additional context enabling improved understanding. While Retrieval Augmentation typically increases latency of LMs (thus hurting distillation efficacy), (2) we provide a practical and effective way of distilling Retrieval Augmentation LLMs. Specifically, we use a novel two-stage distillation approach that allows us to carry over the gains of retrieval augmentation, without suffering the increased compute typically associated with it. (3) We demonstrate the benefits of the proposed approach (QUILL) on a billion-scale, real-world query understanding system resulting in huge gains. Via extensive experiments, including on public benchmarks, we believe this work offers a recipe for practical use of retrieval-augmented query understanding.

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Distinguish Sense from Nonsense: Out-of-Scope Detection for Virtual Assistants
Cheng Qian | Haode Qi | Gengyu Wang | Ladislav Kunc | Saloni Potdar

Out of Scope (OOS) detection in Conversational AI solutions enables a chatbot to handle a conversation gracefully when it is unable to make sense of the end-user query. Accurately tagging a query as out-of-domain is particularly hard in scenarios when the chatbot is not equipped to handle a topic which has semantic overlap with an existing topic it is trained on. We propose a simple yet effective OOS detection method that outperforms standard OOS detection methods in a real-world deployment of virtual assistants. We discuss the various design and deployment considerations for a cloud platform solution to train virtual assistants and deploy them at scale. Additionally, we propose a collection of datasets that replicates real-world scenarios and show comprehensive results in various settings using both offline and online evaluation metrics.

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PLATO-Ad: A Unified Advertisement Text Generation Framework with Multi-Task Prompt Learning
Zeyang Lei | Chao Zhang | Xinchao Xu | Wenquan Wu | Zheng-yu Niu | Hua Wu | Haifeng Wang | Yi Yang | Shuanglong Li

Online advertisement text generation aims at generating attractive and persuasive text ads to appeal to users clicking ads or purchasing products. While pretraining-based models have achieved remarkable success in generating high-quality text ads, some challenges still remain, such as ad generation in low-resource scenarios and training efficiency for multiple ad tasks. In this paper, we propose a novel unified text ad generation framework with multi-task prompt learning, called PLATO-Ad, totackle these problems. Specifically, we design a three-phase transfer learning mechanism to tackle the low-resource ad generation problem. Furthermore, we present a novel multi-task prompt learning mechanism to efficiently utilize a single lightweight model to solve multiple ad generation tasks without loss of performance compared to training a separate model for each task. Finally, we conduct offline and online evaluations and experiment results show that PLATO-Ad significantly outperforms the state-of-the-art on both offline and online metrics. PLATO-Ad has been deployed in a leading advertising platform with 3.5% CTR improvement on search ad descriptions and 10.4% CTR improvement on feed ad titles.

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Dense Feature Memory Augmented Transformers for COVID-19 Vaccination Search Classification
Jai Gupta | Yi Tay | Chaitanya Kamath | Vinh Tran | Donald Metzler | Shailesh Bavadekar | Mimi Sun | Evgeniy Gabrilovich

With the devastating outbreak of COVID-19, vaccines are one of the crucial lines of defense against mass infection in this global pandemic. Given the protection they provide, vaccines are becoming mandatory in certain social and professional settings. This paper presents a classification model for detecting COVID-19 vaccination related search queries, a machine learning model that is used to generate search insights for COVID-19 vaccinations. The proposed method combines and leverages advancements from modern state-of-the-art (SOTA) natural language understanding (NLU) techniques such as pretrained Transformers with traditional dense features. We propose a novel approach of considering dense features as memory tokens that the model can attend to. We show that this new modeling approach enables a significant improvement to the Vaccine Search Insights (VSI) task, improving a strong well-established gradient-boosting baseline by relative +15% improvement in F1 score and +14% in precision.

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Full-Stack Information Extraction System for Cybersecurity Intelligence
Youngja Park | Taesung Lee

Due to rapidly growing cyber-attacks and security vulnerabilities, many reports on cyber-threat intelligence (CTI) are being published daily. While these reports can help security analysts to understand on-going cyber threats,the overwhelming amount of information makes it difficult to digest the information in a timely manner. This paper presents, SecIE, an industrial-strength full-stack information extraction (IE) system for the security domain. SecIE can extract a large number of security entities, relations and the temporal information of the relations, which is critical for cyberthreat investigations. Our evaluation with 133 labeled threat reports containing 108,021 tokens shows thatSecIE achieves over 92% F1-score for entity extraction and about 70% F1-score for relation extraction. We also showcase how SecIE can be used for downstream security applications.

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Deploying Unified BERT Moderation Model for E-Commerce Reviews
Ravindra Nayak | Nikesh Garera

Moderation of user-generated e-commerce content has become crucial due to the large and diverse user base on the platforms. Product reviews and ratings have become an integral part of the shopping experience to build trust among users. Due to the high volume of reviews generated on a vast catalog of products, manual moderation is infeasible, making machine moderation a necessity. In this work, we described our deployed system and models for automated moderation of user-generated content. At the heart of our approach, we outline several rejection reasons for review & rating moderation and explore a unified BERT model to moderate them. We convey the importance of product vertical embeddings for the relevancy of the review for a given product and highlight the advantages of pre-training the BERT models with monolingual data to cope with the domain gap in the absence of huge labelled datasets. We observe a 4.78% F1 increase with less labelled data and a 2.57% increase in F1 score on the review data compared to the publicly available BERT-based models. Our best model In-House-BERT-vertical sends only 5.89% of total reviews to manual moderation and has been deployed in production serving live traffic for millions of users.

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SimANS: Simple Ambiguous Negatives Sampling for Dense Text Retrieval
Kun Zhou | Yeyun Gong | Xiao Liu | Wayne Xin Zhao | Yelong Shen | Anlei Dong | Jingwen Lu | Rangan Majumder | Ji-rong Wen | Nan Duan

Sampling proper negatives from a large document pool is vital to effectively train a dense retrieval model. However, existing negative sampling strategies suffer from the uninformative or false negative problem. In this work, we empirically show that according to the measured relevance scores, the negatives ranked around the positives are generally more informative and less likely to be false negatives. Intuitively, these negatives are not too hard (may be false negatives) or too easy (uninformative). They are the ambiguous negatives and need more attention during training.Thus, we propose a simple ambiguous negatives sampling method, SimANS, which incorporates a new sampling probability distribution to sample more ambiguous negatives.Extensive experiments on four public and one industry datasets show the effectiveness of our approach.We made the code and models publicly available in

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Revisiting and Advancing Chinese Natural Language Understanding with Accelerated Heterogeneous Knowledge Pre-training
Taolin Zhang | Junwei Dong | Jianing Wang | Chengyu Wang | Ang Wang | Yinghui Liu | Jun Huang | Yong Li | Xiaofeng He

Recently, knowledge-enhanced pre-trained language models (KEPLMs) improve context-aware representations via learning from structured relations in knowledge bases, and/or linguistic knowledge from syntactic or dependency analysis. Unlike English, there is a lack of high-performing open-source Chinese KEPLMs in the natural language processing (NLP) community to support various language understanding applications. In this paper, we revisit and advance the development of Chinese natural language understanding with a series of novel Chinese KEPLMs released in various parameter sizes, namely CKBERT (Chinese knowledge-enhanced BERT). Specifically, both relational and linguistic knowledge is effectively injected into CKBERT based on two novel pre-training tasks, i.e., linguistic-aware masked language modeling and contrastive multi-hop relation modeling. Based on the above two pre-training paradigms and our in-house implemented TorchAccelerator, we have pre-trained base (110M), large (345M) and huge (1.3B) versions of CKBERT efficiently on GPU clusters. Experiments demonstrate that CKBERT consistently outperforms strong baselines for Chinese over various benchmark NLP tasks and in terms of different model sizes.

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A Stacking-based Efficient Method for Toxic Language Detection on Live Streaming Chat
Yuto Oikawa | Yuki Nakayama | Koji Murakami

In a live streaming chat on a video streaming service, it is crucial to filter out toxic comments with online processing to prevent users from reading comments in real-time. However, recent toxic language detection methods rely on deep learning methods, which can not be scalable considering inference speed. Also, these methods do not consider constraints of computational resources expected depending on a deployed system (e.g., no GPU resource).This paper presents an efficient method for toxic language detection that is aware of real-world scenarios. Our proposed architecture is based on partial stacking that feeds initial results with low confidence to meta-classifier. Experimental results show that our method achieves a much faster inference speed than BERT-based models with comparable performance.

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End-to-End Speech to Intent Prediction to improve E-commerce Customer Support Voicebot in Hindi and English
Abhinav Goyal | Anupam Singh | Nikesh Garera

Automation of on-call customer support relies heavily on accurate and efficient speech-to-intent (S2I) systems. Building such systems using multi-component pipelines can pose various challenges because they require large annotated datasets, have higher latency, and have complex deployment. These pipelines are also prone to compounding errors. To overcome these challenges, we discuss an end-to-end (E2E) S2I model for customer support voicebot task in a bilingual setting. We show how we can solve E2E intent classification by leveraging a pre-trained automatic speech recognition (ASR) model with slight modification and fine-tuning on small annotated datasets. Experimental results show that our best E2E model outperforms a conventional pipeline by a relative ~27% on the F1 score.

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PILE: Pairwise Iterative Logits Ensemble for Multi-Teacher Labeled Distillation
Lianshang Cai | Linhao Zhang | Dehong Ma | Jun Fan | Daiting Shi | Yi Wu | Zhicong Cheng | Simiu Gu | Dawei Yin

Pre-trained language models have become a crucial part of ranking systems and achieved very impressive effects recently. To maintain high performance while keeping efficient computations, knowledge distillation is widely used. In this paper, we focus on two key questions in knowledge distillation for ranking models: 1) how to ensemble knowledge from multi-teacher; 2) how to utilize the label information of data in the distillation process. We propose a unified algorithm called Pairwise Iterative Logits Ensemble (PILE) to tackle these two questions simultaneously. PILE ensembles multi-teacher logits supervised by label information in an iterative way and achieved competitive performance in both offline and online experiments. The proposed method has been deployed in a real-world commercial search system.

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A Comprehensive Evaluation of Biomedical Entity-centric Search
Elena Tutubalina | Zulfat Miftahutdinov | Vladimir Muravlev | Anastasia Shneyderman

Biomedical information retrieval has often been studied as a task of detecting whether a system correctly detects entity spans and links these entities to concepts from a given terminology. Most academic research has focused on evaluation of named entity recognition (NER) and entity linking (EL) models which are key components to recognizing diseases and genes in PubMed abstracts. In this work, we perform a fine-grained evaluation intended to understand the efficiency of state-of-the-art BERT-based information extraction (IE) architecture as a biomedical search engine. We present a novel manually annotated dataset of abstracts for disease and gene search. The dataset contains 23K query-abstract pairs, where 152 queries are selected from logs of our target discovery platform and PubMed abstracts annotated with relevance judgments. Specifically, the query list also includes a subset of concepts with at least one ambiguous concept name. As a baseline, we use off-she-shelf Elasticsearch with BM25. Our experiments on NER, EL, and retrieval in a zero-shot setup show the neural IE architecture shows superior performance for both disease and gene concept queries.

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Domain Adaptation of Machine Translation with Crowdworkers
Makoto Morishita | Jun Suzuki | Masaaki Nagata

Although a machine translation model trained with a large in-domain parallel corpus achieves remarkable results, it still works poorly when no in-domain data are available. This situation restricts the applicability of machine translation when the target domain’s data are limited. However, there is great demand for high-quality domain-specific machine translation models for many domains. We propose a framework that efficiently and effectively collects parallel sentences in a target domain from the web with the help of crowdworkers.With the collected parallel data, we can quickly adapt a machine translation model to the target domain. Our experiments show that the proposed method can collect target-domain parallel data over a few days at a reasonable cost. We tested it with five domains, and the domain-adapted model improved the BLEU scores to +19.7 by an average of +7.8 points compared to a general-purpose translation model.

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Biomedical NER for the Enterprise with Distillated BERN2 and the Kazu Framework
Wonjin Yoon | Richard Jackson | Elliot Ford | Vladimir Poroshin | Jaewoo Kang

In order to assist the drug discovery/development process, pharmaceutical companies often apply biomedical NER and linking techniques over internal and public corpora. Decades of study of the field of BioNLP has produced a plethora of algorithms, systems and datasets. However, our experience has been that no single open source system meets all the requirements of a modern pharmaceutical company. In this work, we describe these requirements according to our experience of the industry, and present Kazu, a highly extensible, scalable open source framework designed to support BioNLP for the pharmaceutical sector. Kazu is a built around a computationally efficient version of the BERN2 NER model (TinyBERN2), and subsequently wraps several other BioNLP technologies into one coherent system.

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Large-scale Machine Translation for Indian Languages in E-commerce under Low Resource Constraints
Amey Patil | Nikesh Garera

The democratization of e-commerce platforms has moved an increasingly diversified Indian user base to shop online. We have deployed reliable and precise large-scale Machine Translation systems for several Indian regional languages in this work. Building such systems is a challenge because of the low-resource nature of the Indian languages. We develop a structured model development pipeline as a closed feedback loop with external manual feedback through an Active Learning component. We show strong synthetic parallel data generation capability and consistent improvements to the model over iterations. Starting with 1.2M parallel pairs for English-Hindi we have compiled a corpus with 400M+ synthetic high quality parallel pairs across different domains. Further, we need colloquial translations to preserve the intent and friendliness of English content in regional languages, and make it easier to understand for our users. We perform robust and effective domain adaptation steps to achieve colloquial such translations. Over iterations, we show 9.02 BLEU points improvement for English to Hindi translation model. Along with Hindi, we show that the overall approach and best practices extends well to other Indian languages, resulting in deployment of our models across 7 Indian Languages.

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Topic Modeling by Clustering Language Model Embeddings: Human Validation on an Industry Dataset
Anton Eklund | Mona Forsman

Topic models are powerful tools to get an overview of large collections of text data, a situation that is prevalent in industry applications. A rising trend within topic modeling is to directly cluster dimension-reduced embeddings created with pretrained language models. It is difficult to evaluate these models because there is no ground truth and automatic measurements may not mimic human judgment. To address this problem, we created a tool called STELLAR for interactive topic browsing which we used for human evaluation of topics created from a real-world dataset used in industry. Embeddings created with BERT were used together with UMAP and HDBSCAN to model the topics. The human evaluation found that our topic model creates coherent topics. The following discussion revolves around the requirements of industry and what research is needed for production-ready systems.