United Arab Emirates

Interactive argument pair identification is an emerging research task for argument mining, aiming to identify whether two arguments are interactively related. It is pointed out that the context of the argument is essential to improve identification performance. However, current context-based methods achieve limited improvements since the entire context typically contains much irrelevant information. In this paper, we propose a simple contrastive learning framework to solve this problem by extracting valuable information from the context. This framework can construct hard argument-context samples and obtain a robust and uniform representation by introducing contrastive learning. We also propose an argument-context extraction module to enhance information extraction by discarding irrelevant blocks. The experimental results show that our method achieves the state-of-the-art performance on the benchmark dataset. Further analysis demonstrates the effectiveness of our proposed modules and visually displays more compact semantic representations.

EMNLP 2022

A Simple Contrastive Learning Framework for Interactive Argument Pair Identification via Argument-Context Extraction

contrastive learning

interactive argument pair identification

argument mining

poster

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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.

Extractive summarization for long documents is challenging due to the extended structured input context. The long-distance sentence dependency hinders cross-sentence relations modeling, the critical step of extractive summarization. This paper proposes HEGEL, a hypergraph neural network for long document summarization by capturing high-order cross-sentence relations. HEGEL updates and learns effective sentence representations with hypergraph transformer layers and fuses different types of sentence dependencies, including latent topics, keywords coreference, and section structure. We validate HEGEL by conducting extensive experiments on two benchmark datasets, and experimental results demonstrate the effectiveness and efficiency of HEGEL.

HEGEL: Hypergraph Transformer for Long Document Summarization

Document-level relation extraction (DocRE) aims at extracting relations of all entity pairs in a document. A key challenge to DocRE lies in the complex interdependency between the relations of entity pairs. Unlike most prior efforts focusing on implicitly powerful representations, the recently proposed LogiRE (Ru et al., 2021) explicitly captures the interdependency by learning logical rules. However, LogiRE requires extra parameterized modules to reason merely after training backbones, and this disjointed optimization of backbones and extra modules may lead to sub-optimal results. In this paper, we propose MILR, a logic enhanced framework that boosts DocRE by Mining and Injecting Logical Rules. MILR first mines logical rules from annotations based on frequencies. Then in training, consistency regularization
is leveraged as an auxiliary loss to penalize instances that violate mined rules. Finally, MILR infers from a global perspective based on integer programming. Compared with LogiRE, MILR does not introduce extra parameters and injects logical rules during both training and inference. Extensive experiments on two benchmarks demonstrate that MILR not only improves the relation extraction performance (1.1%-3.8% F1) but also makes predictions more logically consistent (over 4.5% Logic). More importantly, MILR also consistently outperforms LogiRE on both counts. Code is available at https://github.com/XingYing-stack/MILR.

Boosting Document-Level Relation Extraction by Mining and Injecting Logical Rules

Multimodal tasks in the fashion domain have significant potential for e-commerce, but involve challenging vision-and-language learning problems—e.g., retrieving a fashion item given a reference image plus text feedback from a user. Prior works on multimodal fashion tasks have either been limited by the data in individual benchmarks, or have leveraged generic vision-and-language pre-training but have not taken advantage of the characteristics of fashion data. Additionally, these works have mainly been restricted to multimodal understanding tasks. To address these gaps, we make two key contributions. First, we propose a novel fashion-specific pre-training framework based on weakly-supervised triplets constructed from fashion image-text pairs. We show the triplet-based tasks are an effective addition to standard multimodal pre-training tasks. Second, we propose a flexible decoder-based model architecture capable of both fashion retrieval and captioning tasks. Together, our model design and pre-training approach are competitive on a diverse set of fashion tasks, including cross-modal retrieval, image retrieval with text feedback, image captioning, relative image captioning, and multimodal categorization.

FaD-VLP: Fashion Vision-and-Language Pre-training towards Unified Retrieval and Captioning

Discontinuous constituency parsing is still kept developing for its efficiency and accuracy are far behind its continuous counterparts. Motivated by the observation that a discontinuous constituent tree can be simply transformed into a pseudo-continuous one by artificially reordering words in the sentence, we propose a novel reordering method, thereby construct fast and accurate discontinuous constituency parsing systems working in continuous way. Specifically, we model the relative position changes of words as a list of actions. By parsing and performing this actions, the corresponding pseudo-continuous sequence is derived. Discontinuous parse tree can be further inferred via integrating a high-performance pseudo-continuous constituency parser. Our systems are evaluated on three classical discontinuous constituency treebanks, achieving new state-of-the-art on two treebanks and showing a distinct advantage in speed.

Reorder and then Parse, Fast and Accurate Discontinuous Constituency Parsing

Thanks to the development of pre-trained language models, multitask learning (MTL) methods achieve a great success in natural language understanding area.
However, current MTL methods pay more attention to task selection or model design to fuse as much knowledge as possible, while intrinsic task correlation is often neglected. It is important to learn sharing strategy among multiple tasks rather than sharing everything.
%The MTL model is directly shared among all the tasks. 
%For example, in traditional MTL methods, the last classification layers or the decoder layers are manually separated. More deeply, 
In this paper, we propose AdapterShare, an adapter differentiation method to explicitly model the task correlation among multiple tasks. AdapterShare is automatically learned based on the gradients on tiny held-out validation data. Compared to single-task learning and fully shared MTL methods, our proposed method obtains obvious performance improvement. Compared to the existing MTL method AdapterFusion, AdapterShare achieves absolute 1.90 average points improvement on five dialogue understanding tasks and 2.33 points gain on NLU tasks.

AdapterShare: Task Correlation Modeling with Adapter Differentiation

Knowledge distillation has been proven effective when customizing small language models for specific tasks. Here, a corpus as `textbook' plays an indispensable role, only through which the teacher can teach the student. Prevailing methods adopt a two-stage distillation paradigm: general distillation first with task-agnostic general corpus and task-specific distillation next with augmented task-specific corpus. We argue that such a paradigm may not be optimal. In general distillation, it's extravagant to let the diverse but desultory general knowledge overwhelms the limited model capacity of the student. While in task-specific distillation, the task corpus is usually limited and narrow, preventing the student from learning enough knowledge. To mitigate the issues in the two gapped corpora, we present a better textbook for the student to learn: contextualized corpus that contextualizes task corpus with large-scale general corpus through relevance-based text retrieval. Experimental results on GLUE benchmark demonstrate that contextualized corpus is the better textbook compared with jointly using general corpus and augmented task-specific corpus. Surprisingly, it enables task-specific distillation from scratch without general distillation while maintaining comparable performance, making it more flexible to customize the student model with desired model size under various computation constraints.

Rethinking Task-Specific Knowledge Distillation: Contextualized Corpus as Better Textbook

Recently there has been an emerging interest in unsupervised vision-and-language pre-training (VLP) that learns multimodal representations without parallel image-caption data. These pioneering works significantly reduce the cost of VLP on data collection and achieve promising results compared to supervised VLP. However, existing unsupervised VLP methods take as input pre-extracted region-based visual features from external object detectors, which both limits flexibility and reduces computational efficiency. In this paper, we explore end-to-end unsupervised VLP with a vision encoder to directly encode images. The vision encoder is pre-trained on image-only data and jointly optimized during multimodal pre-training. To further enhance the learned cross-modal features, we propose a novel pre-training task that predicts which patches contain an object referred to in natural language from the encoded visual features. Extensive experiments on four vision-and-language tasks show that our approach outperforms previous unsupervised VLP methods and obtains new state-of-the-art results.

End-to-End Unsupervised Vision-and-Language Pre-training with Referring Expression Matching

Recently, powerful Transformer architectures have proven superior in generating high-quality sentences. Nevertheless, these models tend to produce dull high-frequency phrases, severely hurting the diversity and novelty of generated text. In this work, we dig into the intrinsic mechanism of this problem and found that sparser attention values in Transformer could improve diversity. To understand such a phenomenon, we first conduct both empirical and theoretical analysis and then attribute it to representation degeneration caused by the attentive mixture of the hidden states during training. We term this process the Trap of Mediocrity. To escape from such a trap, we introduce a novel attention regularization loss to control the sharpness of the attention distribution, which is transparent to model structures and can be easily implemented within 20 lines of python code. We prove that this method could be mathematically regarded as learning a Bayesian approximation of posterior attention. Experiments show that our method improved the diversity and novelty of the generated text while maintaining comparable quality on a variety of conditional and unconditional generation tasks.

Evade the Trap of Mediocrity: Promoting Diversity and Novelty in Text Generation via Concentrating Attention

Scientific extreme summarization (TLDR) aims to form ultra-short summaries of scientific papers. Previous efforts on curating scientific TLDR datasets failed to scale up due to the heavy human annotation and domain expertise required. In this paper, we propose a simple yet effective approach to automatically extracting TLDR summaries for scientific papers from their citation texts. Based on the proposed approach, we create a new benchmark CiteSum without human annotation, which is around 30 times larger than the previous human-curated dataset SciTLDR. We conduct a comprehensive analysis of CiteSum, examining its data characteristics and establishing strong baselines. We further demonstrate the usefulness of CiteSum by adapting models pre-trained on CiteSum (named CITES) to new tasks and domains with limited supervision. For scientific extreme summarization, CITES outperforms most fully-supervised methods on SciTLDR without any fine-tuning and obtains state-of-the-art results with only 128 examples. For news extreme summarization, CITES achieves significant gains on XSum over its base model (not pre-trained on CiteSum), e.g., +7.2 ROUGE-1 zero-shot performance and state-of-the-art few-shot performance. For news headline generation, CITES performs the best among unsupervised and zero-shot methods on Gigaword.

CiteSum: Citation Text-guided Scientific Extreme Summarization and Domain Adaptation with Limited Supervision

While Indic NLP has made rapid advances recently in terms of the availability of corpora and pre-trained models, benchmark datasets on standard NLU tasks are limited. To this end, we introduce INDICXNLI, an NLI dataset for 11 Indic languages. It has been created by high-quality machine translation of the original English XNLI dataset and our analysis attests to the quality of INDICXNLI. By finetuning different pre-trained LMs on this INDICXNLI, we analyze various cross-lingual transfer techniques with respect to the impact of the choice of language models, languages, multi-linguality, mix-language input, etc. These experiments provide us with useful insights into the behaviour of pre-trained models for a diverse set of languages.

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A Simple Contrastive Learning Framework for Interactive Argument Pair Identification via Argument-Context Extraction

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A Simple Contrastive Learning Framework for Interactive Argument Pair Identification via Argument-Context Extraction

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HEGEL: Hypergraph Transformer for Long Document Summarization

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PRESENTATIONS

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RESOURCES

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