Canada

We examine the effects of model size and pre- finetuning in an active learning setting where classifiers are trained from scratch on 14 binary and 3 multi-class text classification tasks. We make an important observation that, in realistic active learning settings, where the human anno- tator and the active learning system operate in asynchronous mode, a compact pre-finetuned 1-layer transformer model with 4.2 million pa- rameters is 30% more label efficient when com- pared to the larger 24-layer 84 million param- eter transformer model. Further, in line with previous studies, we note that pre-finetuning transformer models on related tasks improves label efficiency of downstream tasks by 12%- 50%. The compact pre-finetuned model does not require GPUs, making it a viable solution for large-scale real-time inference with cheaper CPU options.

ACL 2023

Small is the New Big: Pre-finetuned compact models are better for Asynchronous Active Learning
Dantong Liu, Kaushik Pavani and Sunny Dasgupta (lidanton@amazon.com)
Status: Accept

transfer learning

active learning

workshop paper

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
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Following the succession of the recent conferences in our field, ACL 2023 will adopt a hybrid format.
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enormous contributions to the conference over the past year.

Read [**ACL 2023 General Chair's message**](https://docs.google.com/document/d/1WobYM7norbG4dI48s75HfJoD89qgX5a_F-6U8AteLSA/edit?usp=sharing/) in full.

##### **[Conference Handbook](https://2023.aclweb.org/downloads/acl2023-handbook.pdf)**

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing.

Fine-tuning large pre-trained models for downstream tasks can be really expensive. In the past, researchers have proposed various alternatives like adapter and prompt-based methods for tuning these large language models using minimal parameters. However, applying prompt-tuning for smaller language models has not been effective so far and not much work is done in pushing forward soft prompting for
these smaller models. To improve the training efficiency of the language models and reduce the size of tuned parameters, we propose a novel Adapter-based Efficient Prompt Tuning approach (ADEPT). In this paper, we show that tuning the parameters of soft prompts with adapter modules while keeping the rest of the model frozen can be a promising method to optimize smaller language models for downstream tasks. Our method achieves up to 98% performance of full fine-tuning while using only 0.02% of total model parameters.

ADEPT: Adapter-based Efficient Prompt Tuning Approach for Language Models
Aditya Shah, Surendrabikram Thapa, Aneesh Jain and Lifu Huang (surendrabikram@vt.edu)
Status: Accept

Finetuning large language models inflates the costs of NLU applications and remains the bottleneck of development cycles. Recent works in computer vision use data pruning to reduce training time. Pruned data selection with static methods is based on a score calculated for each training example prior to finetuning, which involves important computational overhead. Moreover, the score may not necessarily be representative of sample importance throughout the entire training duration. We propose to address these issues with a refined version of dynamic data pruning, a curriculum which periodically scores and discards unimportant examples during finetuning. Our method leverages an EL2N metric that we extend to the joint intent and slot classification task, and an initial finetuning phase on the full train set. Our results on the GLUE benchmark and four joint NLU datasets show a better time-accuracy trade-off compared to static methods. Our method preserves full accuracy while training on 50% of the data points and reduces computational times by up to 41%. If we tolerate instead a minor drop of accuracy of 1%, we can prune 80% of the training examples for a reduction in finetuning time reaching 66%.

NLU on Data Diets: Dynamic Data Subset Selection for NLP Classification Tasks
Jean-Michel Attendu and Jean-Philippe Corbeil (jeanphilippecorbeil@gmail.com)
Status: Accept

In this work, we provide an analysis on the interactions of the effectiveness of decoding with structural constraints and the amount of available training data for structured prediction tasks in NLP. Our exploration adopts a simple protocol that enforces constraints upon constraint-agnostic local models at testing time. With evaluations on three typical structured prediction tasks (named entity recognition, dependency parsing, and event argument extraction), we find that models trained with less data predict outputs with more structural violations in greedy decoding mode. Incorporating constraints provides consistent performance improvements and such benefits are larger in lower resource scenarios. Moreover, there are similar patterns with regard to the model sizes and more efficient models tend to enjoy more benefits. Finally, we also investigate settings with genre transfer and discover patterns that are related to domain discrepancies.

On the Interactions of Structural Constraints and Data Resources for Structured Prediction
Zhisong Zhang, Emma Strubell and Eduard Hovy (zerozones17@gmail.com)
Status: Accept

Even though many efficient transformers have been proposed, only few such models are available for specialized domains. Additionally, since the pretraining process is extremely costly in general -- but even more so as the sequence length increases -- it is often only in reach of large research labs. One way of making pretraining cheaper is the Replaced Token Detection (RTD) task, by providing more signal during training compared to MLM, since the loss can be computed over all tokens. In this work, we train Longformer models with the efficient RTD task on long-context legal data to showcase that pretraining efficient LMs is possible using less than 12 GPU days. We evaluate the trained models on challenging summarization tasks requiring the model to summarize complex long texts. We find that both the small and base models outperform their baselines on the in-domain BillSum and out-of-domain PubMed tasks in their respective parameter range. We publish our models as a resource for researchers and practitioners.

Can we Pretrain a SotA Legal Language Model on a Budget From Scratch?
Joel Niklaus and Daniele Giofre (joel.niklaus@unibe.ch)
Status: Accept

Conventional Transformer-based Video Ques-tion Answering (VideoQA) approaches gener-ally encode frames independently through oneor more image encoders followed by interac-tion between frames and question. However,such schema would incur significant memoryuse and inevitably slow down the training andinference speed. In this work, we present ahighly efficient approach for VideoQA basedon existing vision-language pre-trained mod-els where we concatenate video frames to an × n matrix and then convert it to one image.By doing so, we reduce the use of the imageencoder from n2 to 1 while maintaining thetemporal structure of the original video. Ex-perimental results on MSRVTT and TrafficQAshow that our proposed approach achievesstate-of-the-art performance with nearly 4×faster speed and only 30% memory use. Weshow that by integrating our approach intoVideoQA systems we can achieve compara-ble, even superior, performance with a sig-nificant speed up for training and inference.We believe the proposed approach can facil-itate VideoQA-related research by reducingthe computational requirements for those whohave limited access to budgets and resources. Our code will be made publicly available forresearch use.

Is a Video worth n × n Images? A Highly Efficient Approach to Transformer-based Video Question Answering
Chenyang Lyu, Tianbo Ji, Yvette Graham and Jennifer Foster (chenyang.lyu2@mail.dcu.ie)
Status: Accept

Scaling language models have revolutionized widespread NLP tasks, yet little comprehensively explored few-shot relation extraction with large language models. In this paper, we investigate in-context learning and data generation for few-shot relation extraction via GPT-3.5 through exhaustive experiments. To enhance few-shot performance, we further propose task-related instructions and schema-constrained data generation. We observe that in-context learning can achieve performance on par with previous prompt learning approaches, and data generation with the large language model can boost previous solutions to obtain new state-of-the-art few-shot results on four widely-studied relation extraction datasets.

How to Unleash the Power of Large Language Models for Few-shot Relation Extraction?
Xin Xu, Yuqi Zhu, Xiaohan Wang and Ningyu Zhang (xxucs@zju.edu.cn)
Status: Accept

Popular models for Knowledge Graph Question Answering (KGQA), including semantic parsing and End-to-End (E2E) models, decode into a constrained space of KG relations. Although E2E models accommodate novel entities at test-time, this constraint means they cannot access novel relations, requiring expensive and time-consuming retraining whenever a new relation is added to the KG. We propose KG-Flex, a new architecture for E2E KGQA that instead decodes into a continuous embedding space of relations, which enables use of novel relations at test-time. KG-Flex is the first to support KG updates with entirely novel triples, free of retraining, while still supporting end-to-end training with simple, weak supervision of (Q, A) pairs. Our architecture saves on time, energy, and data resources for retraining, yet we retain performance on standard benchmarks. We further demonstrate zero-shot use of novel relations, achieving up to 82\% of baseline hit@1 on three QA datasets. KG-Flex can also fine-tune, requiring significantly shorter time than full retraining; fine-tuning on target data for 10% of full training increases hit@1 to 89-100% of baseline.

KGQA Without Retraining
Nick McKenna and Priyanka Sen (nick.mckenna@ed.ac.uk)
Status: Accept

This paper investigates the problem of Named Entity Recognition (NER) for extreme low-resource languages with only a few hundred tagged data samples. NER is a fundamental task in Natural Language Processing (NLP). A critical driver accelerating NER systems' progress is the existence of large-scale language corpora that enable NER systems to achieve outstanding performance in languages such as English and French with abundant training data. However, NER for low-resource languages remains relatively unexplored. In this paper, we introduce Mask Augmented Named Entity Recognition (MANER), a new methodology that leverages the distributional hypothesis of pre-trained masked language models (MLMs) for NER. The <mask> token in pre-trained MLMs encodes valuable semantic contextual information. MANER re-purposes the <mask> token for NER prediction. Specifically, we prepend the <mask> token to every word in a sentence for which we would like to predict the named entity tag. During training, we jointly fine-tune the MLM and a new NER prediction head attached to each <mask> token. We demonstrate that MANER is well-suited for NER in low-resource languages; our experiments show that for 100 languages with as few as 100 training examples, it improves on state-of-the-art methods by up to 48% and by 12% on average on F1 score. We also perform detailed analyses and ablation studies to understand the scenarios that are best-suited to MANER.

MANER: Mask Augmented Named Entity Recognition for Extreme Low-Resource Languages
Shashank Sonkar, Zichao Wang and Richard Baraniuk (shashank.sonkar@rice.edu)
Status: Accept

Recently, compressive text summarisation offers a balance between the conciseness issue of extractive summarisation and the factual hallucination issue of abstractive summarisation. However, most existing compressive summarisation methods are supervised, relying on the expensive effort of creating a new training dataset with corresponding compressive summaries. In this paper, we propose an efficient and interpretable compressive summarisation method that utilises unsupervised dual-agent reinforcement learning to optimise a summary's semantic coverage and fluency by simulating human judgment on summarisation quality. Our model consists of an extractor agent and a compressor agent, and both agents have a multi-head attentional pointer-based structure. The extractor agent first chooses salient sentences from a document, and then the compressor agent compresses these extracted sentences by selecting salient words to form a summary without using reference summaries to compute the summary reward. To our best knowledge, this is the first work on unsupervised compressive summarisation. Experimental results on three widely used datasets (e.g., Newsroom, CNN/DM, and XSum) show that our model achieves promising performance and a significant improvement on Newsroom in terms of the ROUGE metric, as well as interpretability of semantic coverage of summarisation results.

Efficient and Interpretable Compressive Text Summarisation with Unsupervised Dual-Agent Reinforcement Learning
Peggy Tang, Junbin Gao, Lei Zhang and Zhiyong Wang (peggy.tang@sydney.edu.au)
Status: Accept

The recently introduced FNet model (Lee-Thorp et al., 2022) computes a two-dimensional discrete Fourier transform (DFT) of a sequence- length-by-hidden-dimension-sized representation of its input. Because it is equally efficient to compute the DFT of any reshaping of this input matrix, we investigate the extent to which increasing the frequency resolution in one dimension (at the expense of the other) affects task performance. We consider the LRA tasks (Tay et al., 2021) considered by Lee-Thorpe et al., as well as the more practical setting of using FNet as the encoder in a machine translation (MT) model. We find that frequency resolution has a marked task-dependent effect on performance, allowing us to largely outperform standard FNet on our tasks, and suggesting that resolution should be carefully tuned before deploying FNet.

Exploring the Effect of Frequency Resolution in FNet
Gregory Szumel, Ghazal Khalighinejad, Rickard Stureborg and Sam Wiseman (gks9@duke.edu)
Status: Accept

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Small is the New Big: Pre-finetuned compact models are better for Asynchronous Active Learning Dantong Liu, Kaushik Pavani and Sunny Dasgupta (lidanton@amazon.com) Status: Accept

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ADEPT: Adapter-based Efficient Prompt Tuning Approach for Language Models Aditya Shah, Surendrabikram Thapa, Aneesh Jain and Lifu Huang (surendrabikram@vt.edu) Status: Accept

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