ymoslem/acl-abstracts huggingface dataset:
Creator Context for Tweet Recommendation (2023)

When discussing a tweet, people usually not only refer to the content it delivers, but also to the person behind the tweet. In other words, grounding the interpretation of the tweet in the context of its creator plays an important role in deciphering the true intent and the importance of the tweet. In this paper, we attempt to answer the question of how creator context should be used to advance tweet understanding. Specifically, we investigate the usefulness of different types of creator context, and examine different model structures for incorporating creator context in tweet modeling. We evaluate our tweet understanding models on a practical use case {--} recommending relevant tweets to news articles. This use case already exists in popular news apps, and can also serve as a useful assistive tool for journalists. We discover that creator context is essential for tweet understanding, and can improve application metrics by a large margin. However, we also observe that not all creator contexts are equal. Creator context can be time sensitive and noisy. Careful creator context selection and deliberate model structure design play an important role in creator context effectiveness.
AdaBERT-CTC: Leveraging BERT-CTC for Text-Only Domain Adaptation in ASR (2023)

End-to-end (E2E) automatic speech recognition (ASR) models are becoming increasingly popular in commercial applications, such as virtual assistants, closed captioning, and dictation systems. The accuracy of the ASR is crucial to their success. However, E2E models still struggle to recognize out-of-domain words such as proper nouns and domain-specific terms. In this paper we introduce AdaBERT-CTC, a domain adaptation technique that relies solely on textual data. Our method allows for text-only adaptation by fine-tuning a pre-trained self-supervised text encoder model. Additionally, we show that our method can be made parameter-efficient by adding bottleneck adapters to the pre-trained model. This allows for adaptation with less than a 5{\%} increase in parameters and minimal computational overhead during inference. We demonstrate that our approach outperforms the base BERT-CTC model by up to 14{\%} relative word error rate improvement on several out-of-domain, publicly available datasets.
Conversing with databases: Practical Natural Language Querying (2023)

In this work, we designed, developed and released in production DataQue {--} a hybrid NLQ (Natural Language Querying) system for conversational DB querying. We address multiple practical problems that are not accounted for in public Text-to-SQL solutions {--} numerous complex implied conditions in user questions, jargon and abbreviations, custom calculations, non-SQL operations, a need to inject all those into pipeline fast and to have guaranteed parsing results for demanding users, cold-start problem. The DataQue processing pipeline for Text-to-SQL translation consists of 10-15 model-based and rule-based components that allows to tightly control the processing.
AART: AI-Assisted Red-Teaming with Diverse Data Generation for New LLM-powered Applications (2023)

Adversarially testing large language models (LLMs) is crucial for their safe and responsible deployment in practice. We introduce an AI-assisted approach for automated generation of adversarial evaluation datasets to test the safety of LLM generations on new downstream applications. We call it AART AI-assisted Red-Teaming - an automated alternative to current manual red-teaming efforts. AART offers a data generation and augmentation pipeline of reusable and customizable recipes that reduce significantly human effort and enable integration of adversarial testing earlier in new product development. AART generates evaluation datasets with high diversity of content characteristics critical for effective adversarial testing (e.g. sensitive and harmful concepts, specific to a wide range of cultural and geographic regions and application scenarios). The data generation is steered by AI-assisted recipes to define, scope and prioritize diversity within a new application context. This feeds into a structured LLM-generation process that scales up evaluation priorities. This provides transparency of developers evaluation intentions and enables quick adaptation to new use cases and newly discovered model weaknesses. Compared to some of the state-of-the-art tools AART shows promising results in terms of concept coverage and data quality.
Speakerly: A Voice-based Writing Assistant for Text Composition (2023)

We present Speakerly, a new real-time voice-based writing assistance system that helps users with text composition across various use cases such as emails, instant messages, and notes. The user can interact with the system through instructions or dictation, and the system generates a well-formatted and coherent document. We describe the system architecture and detail how we address the various challenges while building and deploying such a system at scale. More specifically, our system uses a combination of small, task-specific models as well as pre-trained language models for fast and effective text composition while supporting a variety of input modes for better usability.
Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? A Study on Several Typical Tasks (2023)

The most recent large language models (LLMs) such as ChatGPT and GPT-4 have shown exceptional capabilities of generalist models, achieving state-of-the-art performance on a wide range of NLP tasks with little or no adaptation. How effective are such models in the finance domain? Understanding this basic question would have a significant impact on many downstream financial analytical tasks. In this paper, we conduct empirical studies and provide experimental evidences of their performance on a wide variety of financial text analytical problems, using eight benchmark datasets from five categories of tasks. We report both the strengths and limitations of the current models by comparing them to the state-of-the-art fine-tuned approaches and the recently released domain-specific pretrained models. We hope our study can help to understand the capability of the existing models in the financial domain and facilitate further improvements.
CL-QR: Cross-Lingual Enhanced Query Reformulation for Multi-lingual Conversational AI Agents (2023)

The growing popularity of conversational AI agents such as Alexa, Google Assistant, and Siri rely on accurate spoken language comprehension. The query reformulation (QR) method, which reformulates defective user queries, has been broadly adopted to mitigate the challenges posed by understanding user`s intent from imperfect spoken recognition result. However, due to the scarcity of non-English QR labels, providing high-quality QR for non-English users still remains a challenge. This work proposes a novel cross-lingual QR framework, CL-QR, to leverage the abundant reformulation resources in English to improve non-English QR performance. The proposed work also proposes a Module-wise Mutually-supervised Feedback learning (MMF) algorithm to enable the continually self-improving of the CL-QR, which alleviates the lack of cross-lingual QR training data and enhances the delivery of high-quality reformulations learned in English for multilingual queries. Both offline evaluation and online A/B testing demonstrates the effectiveness of the proposed method.
Improving Contextual Query Rewrite for Conversational AI Agents through User-preference Feedback Learning (2023)

Contextual query rewriting (CQR) is a crucial component in Conversational AI agents, leveraging the contextual information from previous user-agent conversations to improve the comprehension of current user intent. However, traditional CQR methods often concentrate on supervised fine-tuning only, neglecting the opportunities to learn from user feedback to align with user preferences. Inspired by recent advances in learning from human feedback (LHF), this paper proposes a novel Preference Aligned Contextual Query Rewriting (PA-CQR) framework to enhance the CQR model`s capability in generating user preference-aligned rewrites. This paper also investigates the efficacy of various state-of-the-art feedback learning algorithms on the CQR task, and proposes a novel Dynamic Direct Preference Optimization (Dynamic DPO) algorithm to better adapt the DPO algorithm to large-scale CQR training. Experiments on large-scale real-world CQR data set demonstrate the superiority of the proposed PA-CQR framework and the Dynamic DPO.
Scaling Neural ITN for Numbers and Temporal Expressions in Tamil: Findings for an Agglutinative Low-resource Language (2023)

ITN involves rewriting the verbalised form of text from spoken transcripts to its corresponding written form. The task inherently expects challenges in identifying ITN entries due to spelling variations in words arising out of dialects, transcription errors etc. Additionally, in Tamil, word boundaries between adjacent words in a sentence often get obscured due to Punarchi, i.e. phonetic transformation of these boundaries. Being morphologically rich, the words in Tamil show a high degree of agglutination due to inflection and clitics. The combination of such factors leads to a high degree of surface-form variations, making scalability with pure rule-based approaches difficult. Instead, we experiment with fine-tuning three pre-trained neural LMs, consisting of a seq2seq model (s2s), a non-autoregressive text editor (NAR) and a sequence tagger + rules combination (tagger). While the tagger approach works best in a fully-supervised setting, s2s performs the best (98.05 F-Score) when augmented with additional data, via bootstrapping and data augmentation (DA{\&}B). S2S reports a cumulative percentage improvement of 20.1 {\%}, and statistically significant gains for all our models with DA{\&}B. Compared to a fully supervised setup, bootstrapping alone reports a percentage improvement as high as 14.12 {\%}, even with a small seed set of 324 ITN entries.
EELBERT: Tiny Models through Dynamic Embeddings (2023)

We introduce EELBERT, an approach for compression of transformer-based models (e.g., BERT), with minimal impact on the accuracy of downstream tasks. This is achieved by replacing the input embedding layer of the model with dynamic, i.e. on-the-fly, embedding computations. Since the input embedding layer occupies a large portion of the model size, especially for the smaller BERT variants, replacing this layer with an embedding computation function helps us reduce the model size significantly. Empirical evaluation on the GLUE benchmark shows that our BERT variants (EELBERT) suffer minimal regression compared to the traditional BERT models. Through this approach, we are able to develop our smallest model UNO-EELBERT, which achieves a GLUE score within 4{\%} of fully trained BERT-tiny, while being 15x smaller (1.2 MB) in size.
Gold Standard Bangla OCR Dataset: An In-Depth Look at Data Preprocessing and Annotation Processes (2023)

This research paper focuses on developing an improved Bangla Optical Character Recognition (OCR) system, addressing the challenges posed by the complexity of Bangla text structure, diverse handwriting styles, and the scarcity of comprehensive datasets. Leveraging recent advancements in Deep Learning and OCR techniques, we anticipate a significant enhancement in the performance of Bangla OCR by utilizing a large and diverse collection of labeled Bangla text image datasets. This study introduces the most extensive gold standard corpus for Bangla characters and words, comprising over 4 million human-annotated images. Our dataset encompasses various document types, such as Computer Compose, Letterpress, Typewriters, Outdoor Banner-Poster, and Handwritten documents, gathered from diverse sources. The entire corpus has undergone meticulous human annotation, employing a controlled annotation procedure consisting of three-step annotation and one-step validation, ensuring adherence to gold standard criteria. This paper provides a comprehensive overview of the complete data collection procedure. The ICT Division, Government of the People`s Republic of Bangladesh, will make the dataset publicly available, facilitating further research and development in Bangla OCR and related domains.
PILLOW: Enhancing Efficient Instruction Fine-tuning via Prompt Matching (2023)

Instruction fine-tuning has conventionally been employed to adapt Large Language Models (LLMs) to a variety of diverse tasks. Nonetheless, this technique often necessitates substantial computational resources, making it impractical for deployment by individuals or small-scale entities. Recently, Low-Rank Adaptation (LoRA) has become a promising alternative, offering tuning capabilities with reduced resource overhead. However, attaining satisfactory performance through the fine-tuning of LoRA is a non-trivial challenge. In this paper, we propose PILLOW, which aims to improve LoRA`s performance by leveraging LLM`s in-context learning capability through prompt matching via reinforcement learning in resource-constrained environments. Specifically, PILLOW incorporates a matching network that selects prompts from a user-defined pool, concatenates the optimal prompts given the user instruction, and performs inference using the LoRA-fine-tuned LLMs. Compared with typical instruction fine-tuning methods, PILLOW exhibits commensurate performance on various evaluation metrics, utilizing only consumer-grade GPU resources and exhibiting a large increase in training efficiency.
Welcome to the Real World: Efficient, Incremental and Scalable Key Point Analysis (2023)

Key Point Analysis (KPA) is an emerging summarization framework, which extracts the main points from a collection of opinions, and quantifies their prevalence. It has been successfully applied to diverse types of data, including arguments, user reviews and survey responses. Despite the growing academic interest in KPA, little attention has been given to the practical challenges of implementing a KPA system in production. This work presents a deployed KPA system, which regularly serves multiple teams in our organization. We discuss the main challenges we faced while building a real-world KPA system, as well as the architecture and algorithmic improvements we developed to address these challenges. Specifically, we focus on efficient matching of sentences to key points, incremental processing, scalability and resiliency. The value of our contributions is demonstrated in an extensive set of experiments, over five existing and novel datasets. Finally, we describe several use cases of the deployed system, which illustrate its practical value.
Automatic Linking of Judgements to UK Supreme Court Hearings (2023)

One the most important archived legal material in the UK is the Supreme Court published judgements and video recordings of court sittings for the decided cases. The impact of Supreme Court published material extends far beyond the parties involved in any given case as it provides landmark rulings on arguable points of law of the greatest public and constitutional importance. However, the recordings of a case are usually very long which makes it both time and effort consuming for legal professionals to study the critical arguments in the legal deliberations. In this research, we summarise the second part of a combined research-industrial project for building an automated tool designed specifically to link segments in the text judgement to semantically relevant timespans in the videos of the hearings. The tool is employed as a User-Interface (UI) platform that provides a better access to justice by bookmarking the timespans in the videos which contributed to the final judgement of the case. We explain how we employ AI generative technology to retrieve the relevant links and show that the customisation of the GPT text embeddings to our dataset achieves the best accuracy for our automatic linking system.
Automatic Marketing Theme and Commodity Construction System for E-commerce (2023)

When consumers' shopping needs are concentrated, they are more interested in the collection of commodities under the specific marketing theme. Therefore, mining marketing themes and their commodities collections can help customers save shopping costs and improve user clicks and purchases for recommendation system. However, the current system invites experts to write marketing themes and select the relevant commodities, which suffer from difficulty in mass production, poor timeliness and low online indicators. Therefore, we propose a automatic marketing theme and commodity construction system, which can not only generate popular marketing themes and select the relevant commodities automatically, but also improve the theme online effectiveness in the recommendation system. Specifically, we firstly utilize the pretrained language model to generate the marketing themes. And then, we utilize the theme-commodity consistency module to select the relevant commodities for the above generative theme. What`s more, we also build the indicator simulator to evaluate the effectiveness of the above generative theme. When the indicator is lower, the above selective commodities will be input into the theme-rewriter module to generate more efficient marketing themes. Finally, we utilize the human screening to control the system quality. Both the offline experiments and online A/B test demonstrate the superior performance of our proposed system compared with state-of-the-art methods.
Towards Safer Operations: An Expert-involved Dataset of High-Pressure Gas Incidents for Preventing Future Failures (2023)

This paper introduces a new IncidentAI dataset for safety prevention. Different from prior corpora that usually contain a single task, our dataset comprises three tasks: named entity recognition, cause-effect extraction, and information retrieval. The dataset is annotated by domain experts who have at least six years of practical experience as high-pressure gas conservation managers. We validate the contribution of the dataset in the scenario of safety prevention. Preliminary results on the three tasks show that NLP techniques are beneficial for analyzing incident reports to prevent future failures. The dataset facilitates future research in NLP and incident management communities. The access to the dataset is also provided (The IncidentAI dataset is available at: https://github.com/Cinnamon/incident-ai-dataset).
An Auxiliary Task Boosted Multi-task Learning Method for Service Account Retrieval with Limited Human Annotation (2023)

Service accounts, including organizations' official accounts and mini-programs, provide various convenient services for users, and have become crucial components of a number of applications. Therefore, retrieving service accounts quickly and accurately is vital. However, this task suffers from the problem of limited human annotation, i.e., manually assessing account functionality and assigning ratings based on user experience is both labor-intensive and time-consuming. To this end, this paper proposes a novel approach, the Auxiliary task Boosted Multi-Task Learning method (AuxBoost-MTL). Specifically, the proposed method introduces multiple auxiliary tasks, which is able to utilized the log data from our application as supervision, and enhance the performance of the main task, service account retrieval. Furthermore, we introduce an Adaptive Hierarchical Fusion Module (AHF module) into our approach. This module is designed to adaptively perform hierarchical fusion of embeddings from auxiliary tasks into the main task, thereby enhancing the model efficacy. Experiments on two real-world industrial datasets demonstrate the effectiveness of our proposed approach.
VKIE: The Application of Key Information Extraction on Video Text (2023)

Extracting structured information from videos is critical for numerous downstream applications in the industry. In this paper, we define a significant task of extracting hierarchical key information from visual texts on videos. To fulfill this task, we decouple it into four subtasks and introduce two implementation solutions called PipVKIE and UniVKIE. PipVKIE sequentially completes the four subtasks in continuous stages, while UniVKIE is improved by unifying all the subtasks into one backbone. Both PipVKIE and UniVKIE leverage multimodal information from vision, text, and coordinates for feature representation. Extensive experiments on one well-defined dataset demonstrate that our solutions can achieve remarkable performance and efficient inference speed.
Investigating the Role and Impact of Disfluency on Summarization (2023)

Contact centers handle both chat and voice calls for the same domain. As part of their workflow, it is a standard practice to summarize the conversations once they conclude. A significant distinction between chat and voice communication lies in the presence of disfluencies in voice calls, such as repetitions, restarts, and replacements. These disfluencies are generally considered noise for downstream natural language understanding (NLU) tasks. While a separate summarization model for voice calls can be trained in addition to chat specific model for the same domain, it requires manual annotations for both the channels and adds complexity arising due to maintaining two models. Therefore, it`s crucial to investigate if a model trained on fluent data can handle disfluent data effectively. While previous research explored impact of disfluency on question-answering and intent detection, its influence on summarization is inadequately studied. Our experiments reveal up to 6.99-point degradation in Rouge-L score, along with reduced fluency, consistency, and relevance when a fluent-trained model handles disfluent data. Replacement disfluencies have the highest negative impact. To mitigate this, we examine Fused-Fine Tuning by training the model with a combination of fluent and disfluent data, resulting in improved performance on both public and real-life datasets. Our work highlights the significance of incorporating disfluency in training summarization models and its advantages in an industrial setting.
InsightNet : Structured Insight Mining from Customer Feedback (2023)

We propose InsightNet, a novel approach for the automated extraction of structured insights from customer reviews. Our end-to-end machine learning framework is designed to overcome the limitations of current solutions, including the absence of structure for identified topics, non-standard aspect names, and lack of abundant training data. The proposed solution builds a semi-supervised multi-level taxonomy from raw reviews, a semantic similarity heuristic approach to generate labelled data and employs a multi-task insight extraction architecture by fine-tuning an LLM. InsightNet identifies granular actionable topics with customer sentiments and verbatim for each topic. Evaluations on real-world customer review data show that InsightNet performs better than existing solutions in terms of structure, hierarchy and completeness. We empirically demonstrate that InsightNet outperforms the current state-of-the-art methods in multi-label topic classification, achieving an F1 score of 0.85, which is an improvement of 11{\%} F1-score over the previous best results. Additionally, InsightNet generalises well for unseen aspects and suggests new topics to be added to the taxonomy.
E2E Spoken Entity Extraction for Virtual Agents (2023)

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