Through reading the documentation in the context, tool-using language models can dynamically extend their capability using external tools. The cost is that we have to input lengthy documentation every time the model needs to use the tool, occupying the input window as well as slowing down the decoding process.

Given the progress in general-purpose compression, soft context compression is a suitable approach to alleviate the problem. However, when compressing tool documentation, existing methods suffer from the weaknesses of key information loss (specifically, tool/parameter name errors) and difficulty in adjusting the length of compressed sequences based on documentation lengths.

To address these problems, we propose two strategies for compressing tool documentation into concise and precise summary sequences for tool-using language models. 1) Selective compression strategy mitigates key information loss by deliberately retaining key information as raw text tokens. 2) Block compression strategy involves dividing tool documentation into short chunks and then employing a fixed-length compression model to achieve variable-length compression. This strategy facilitates the flexible adjustment of the compression ratio.

Results on API-Bank and APIBench show that our approach reaches a performance comparable to the upper-bound baseline under up to 16x compression ratio.

Concise and Precise Context Compression for Tool-Using Language Models

Prompting methods recently achieve impressive success in few-shot learning. These methods modify input samples with prompt sentence pieces, and decode label tokens to map samples to corresponding labels. However, such a paradigm is very inefficient for the task of slot tagging. Since slot tagging samples are multiple consecutive words in a sentence, the prompting methods have to enumerate all n-grams token spans to find all the possible slots, which greatly slows down the prediction. To tackle this, we introduce an inverse paradigm for prompting. Different from the classic prompts mapping tokens to labels, we reversely predict slot values given slot types. Such inverse prompting only requires a one-turn prediction for each slot type and greatly speeds up the prediction. Besides, we propose a novel Iterative Prediction Strategy, from which the model learns to refine predictions by considering the relations between different slot types. We find, somewhat surprisingly, the proposed method not only predicts faster but also significantly improves the effect (improve over 6.1 F1-scores on 10-shot setting) and achieves new state-of-the-art performance.

Inverse is Better! Fast and Accurate Prompt for Few-shot Slot Tagging

In this paper, we investigate few-shot joint
learning for dialogue language understanding.
Most existing few-shot models learn a single task each time with only a few examples. However, dialogue language understanding contains two closely related tasks, i.e., intent detection and slot filling, and often benefits from jointly learning the two tasks. This
calls for new few-shot learning techniques that
are able to capture task relations from only a
few examples and jointly learn multiple tasks.
To achieve this, we propose a similarity-based
few-shot learning scheme, named Contrastive
Prototype Merging network (ConProm), that
learns to bridge metric spaces of intent and
slot on data-rich domains, and then adapt the
bridged metric space to specific few-shot domain. Experiments on two public datasets,
Snips and FewJoint, show that our model significantly outperforms the strong baselines in
one and five shots settings.

Learning to Bridge Metric Spaces: Few-shot Joint Learning of Intent Detection and Slot Filling

Slot filling, a fundamental module of spoken language understanding, often suffers from insufficient quantity and diversity of training data. To remedy this, we propose a novel Cluster-to-Cluster generation framework for Data Augmentation (DA), named C2C-GenDA. It enlarges the training set by reconstructing existing utterances into alternative expressions while keeping semantic. Different from previous DA works that reconstruct utterances one by one independently, C2C-GenDA jointly encodes multiple existing utterances of the same semantics and simultaneously decodes multiple unseen expressions. Jointly generating multiple new utterances allows to consider the relations between generated instances and encourages diversity. Besides, encoding multiple existing utterances endows C2C with a wider view of existing expressions, helping to reduce generation that duplicates existing data. Experiments on ATIS and Snips datasets show that instances augmented by C2C-GenDA improve slot filling by 7.99 (11.9%â†‘) and 5.76 (13.6%â†‘) F-scores respectively, when there are only hundreds of training utterances. Code: https://github.com/Sanyuan-Chen/C2C-DA.

C2C-GenDA: Cluster-to-Cluster Generation for Data Augmentation of Slot Filling

In this paper, we study the few-shot multi-label classification for user intent detection. For multi-label intent detection, state-of-the-art work estimates label-instance relevance scores and uses a threshold to select multiple associated intent labels. To determine appropriate thresholds with only a few examples, we first learn universal thresholding experience on data-rich domains, and then adapt the thresholds to certain few-shot domains with a calibration based on nonparametric learning. For better calculation of label-instance relevance score, we introduce label name embedding as anchor points in representation space, which refines representations of different classes to be well-separated from each other. Experiments on two datasets show that the proposed model significantly outperforms strong baselines in both one-shot and five-shot settings.

Few-Shot Learning for Multi-Label Intent Detection

Image augmentation is a common mechanism to alleviate data scarcity in image classification. Existing image augmentation methods consist of two categories. Perturbation-based methods apply pre-defined perturbations to augment an original image, but only locally vary the image, thus lacking image diversity. In contrast, generative methods bring more image diversity in the augmented images but may not preserve semantic consistency, thus incorrectly change the essential semantics of the original image. To balance image diversity and semantic consistency in augmented images, we propose SGID, a Semantic-guided Generative Image augmentation method with Diffusion models for image classification. Specifically, SGID employs image labels and captions as semantic guidance to generate augmented images that preserve the essential semantics in original images and simultaneously bring good image diversity. Experimental results show that SGID outperforms the best augmentation baseline by 1.72% on ResNet-50 (scratch), 0.33% on ViT (ImageNet-21k), and 0.14% on ViT (LAION-2B). Moreover, SGID further improves baseline performance as a plug-in. We demonstrate the semantic consistency and image diversity of SGID through quantitative human and automated evaluations, as well as qualitative case studies.

Semantic-Guided Generative Image Augmentation Method with Diffusion Models for Image Classification

Findings In-Person Poster Session 1

poster

### Welcome!
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ACL 2024

The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

Posters: Dialogue and Interactive Systems

findings / work in progress

# Welcome everyone to ACL 2022!

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ACL 2022

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 (NLP). 

Findings Papers - Dialog and Interactive Systems

technical paper

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IJCNLP-AACL 2021

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Main Track

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AAAI 2021

Main Track - Computer Vision

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AAAI 2024

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. 

Yutai Hou

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SHORT BIO

Presentations

Concise and Precise Context Compression for Tool-Using Language Models

Inverse is Better! Fast and Accurate Prompt for Few-shot Slot Tagging

Learning to Bridge Metric Spaces: Few-shot Joint Learning of Intent Detection and Slot Filling

C2C-GenDA: Cluster-to-Cluster Generation for Data Augmentation of Slot Filling

Few-Shot Learning for Multi-Label Intent Detection

Semantic-Guided Generative Image Augmentation Method with Diffusion Models for Image Classification

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