Singapore

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

EMNLP 2023

AdapterDistillation: Non-Destructive Task Composition with Knowledge Distillation | VIDEO

adapter learning

dialogue system

knowledge distillation

## Welcome to EMNLP 2022!
I am delighted to welcome you to EMNLP 2022! I believe this conference has been complicated beyond any precedent. Over the past year, it’s been thrilling to see the organization team approach each new puzzle with creativity and enthusiasm. We hope that those participating in Abu Dhabi as well as those joining remotely will leave the conference feeling newly inspired by the program and newly connected to our ever-growing community. Following EMNLP 2021 and major NLP conferences since, EMNLP 2022 is “hybrid,” serving both virtual and in-person participants.

Our key innovations for EMNLP 2022 include:

* EMNLP 2022 is “hybrid” in a second sense, as well: we allowed both direct and rolling review paper submissions, building on the pilot experiment of EMNLP 2021, which considered a small number of ARR submissions. 
* Familiar from NAACL but new to EMNLP, we’ve added an industry track.
* During the conference, “portals” will link virtual poster sessions to in-person conference participants during poster sessions each day.
* The first ACL-family conference in the United Arab Emirates.

 *Message from Noah A. Smith, University of Washington and Allen Institute for AI, Seattle, Washington, USA* 
***EMNLP 2022 General Chair***
 
[![](https://assets.underline.io/uploads/markdown_image/1/image/9eec7d4a287ee18c278b08229290aa83.png)](https://drive.google.com/file/d/1OlPv6QBeo62VVTughj2jkiLeyHd1WnUt/view)
 
[![](https://assets.underline.io/uploads/markdown_image/1/image/a3db7a768409f05192210d98601edb25.png)](https://emnlp2022.rocket.chat/)

To access this site you need to register. Please register [here](https://2022.emnlp.org/registration/).

Register here

EMNLP 2022

Welcome!
EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

technical paper

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

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

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

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.

An Auxiliary Task Boosted Multi-task Learning Method for Service Account Retrieval with Limited Human Annotation | VIDEO

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.

Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? A Study on Several Typical Tasks

Recently, diffusion-based deep generative models (e.g., Stable Diffusion) have shown impressive results in text-to-image synthesis. However, current text-to-image models often require multiple passes of prompt engineering by humans in order to produce satisfactory results for real-world applications. We propose BeautifulPrompt, a deep generative model to produce high-quality prompts from very simple raw descriptions, which enables diffusion-based models to generate more beautiful images. In our work, we first fine-tuned the BeautifulPrompt model over low-quality and high-quality collecting prompt pairs. Then, to ensure that our generated prompts can generate more beautiful images, we further propose a Reinforcement Learning with Visual AI Feedback technique to fine-tune our model to maximize the reward values of the generated prompts, where the reward values are calculated based on the PickScore and the Aesthetic Scores. Our results demonstrate that learning from visual AI feedback promises the potential to improve the quality of generated prompts and images significantly. We further showcase the integration of BeautifulPrompt to a cloud-native AI platform to provide better text-to-image generation service in the cloud.

BeautifulPrompt: Towards Automatic Prompt Engineering for Text-to-Image Synthesis

Large language models (LLMs) have demonstrated remarkable performance by following natural language instructions without fine-tuning them on domain-specific tasks and data. However, leveraging LLMs for domain-specific question answering suffers from severe limitations. The generated answer tends to hallucinate due to the training data collection time (when using off-the-shelf), complex user utterance and wrong retrieval (in retrieval-augmented generation). Furthermore, due to the lack of awareness about the domain and expected output, such LLMs may generate unexpected and unsafe answers that are not tailored to the target domain. In this paper, we propose CarExpert, an in-car retrieval-augmented conversational question-answering system leveraging LLMs for different tasks. Specifically, CarExpert employs LLMs to control the input, provide domain-specific documents to the extractive and generative answering components, and controls the output to ensure safe and domain-specific answers. A comprehensive empirical evaluation exhibits that CarExpert outperforms state-of-the-art LLMs in generating natural, safe and car-specific answers.

CarExpert: Leveraging Large Language Models for In-Car Conversational Question Answering | VIDEO

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

Compute-Efficient Churn Reduction for Conversational Agents

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.

Conversing with databases: Practical Natural Language Querying

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

V52-A: Coordinated Replay Sample Selection for Continual Federated Learning

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.

Creator Context for Tweet Recommendation

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

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

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

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