Thailand

Existing Natural Language Understanding (NLU) models have been shown to incorporate dataset biases leading to strong performance on in-distribution (ID) test sets but poor performance on out-of-distribution (OOD) ones. We introduce a simple yet effective debiasing framework whereby the shallow representations of the main model are used to derive a bias model and both models are trained simultaneously. We demonstrate on three well-studied NLU tasks that despite its simplicity, our method leads to competitive OOD results. It significantly outperforms other debiasing approaches on two tasks, while still delivering high in-distribution performance.


ACL-IJCNLP 2021

End-to-End Self-Debiasing Framework for Robust NLU Training

bias

robustness

**Welcome to ACL-IJCNLP 2021!**

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP). 

As in previous years, the program of the conference includes a poster session, tutorials, workshops and demonstrations in addition to the main conference.


We were able to keep the registration fees similar to those charged for the virtual ACL 2020. The one fee allows attendance at the main conference and any/all tutorials and workshops. These fees would be $125 Regular Early and $175 Regular Late; $50 Student Early and $75 Student Late. Early registration closes at midnight July 11, 2021 (Eastern Daylight Time).

**Reminder:** It is ACL’s policy that at least one author of each accepted paper (including ACL Finding papers) must register for the conference.

**Reminder2:** Underline site will open closer to the event. If you already registered you will receive access detail

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The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

workshop paper

Despite the prominence of neural abstractive summarization models, we know little about how they actually form summaries and how to understand where their decisions come from.
We propose a two-step method to interpret summarization model decisions. 
We first analyze the model's behavior by ablating the full model to categorize each decoder decision into one of several generation modes: roughly, is the model behaving like a language model, is it relying heavily on the input, or is it somewhere in between?
After isolating decisions that do depend on the input, we explore interpreting these decisions using several different attribution methods.
We compare these techniques based on their ability to select content and reconstruct the model's predicted token from perturbations of the input, thus revealing whether highlighted attributions are truly important for the generation of the next token.
While this machinery can be broadly useful even beyond summarization, we specifically demonstrate its capability to identify phrases the summarization model has memorized and determine where in the training pipeline this memorization happened, as well as study complex generation phenomena like sentence fusion on a per-instance basis.

Dissecting Generation Modes for Abstractive Summarization Models via Ablation and Attribution

In this paper, we present a conceptually simple while empirically powerful framework for abstractive summarization, SimCLS, which can bridge the gap between the learning objective and evaluation metrics resulting from the currently dominated sequence-to-sequence learning framework by formulating text generation as a reference-free evaluation problem (i.e., quality estimation) assisted by contrastive learning.
Experimental results show that, with minor modification over existing top-scoring systems, SimCLS can improve the performance of existing top-performing models by a large margin.
Particularly, 2.51 absolute improvement against BART and 2.50 over PEGASUS w.r.t ROUGE-1 on the CNN/DailyMail dataset, driving the state-of-the-art performance to a new level.
We have open-sourced our codes and results: https://github.com/yixinL7/SimCLS.
Results of our proposed models have been deployed into ExplainaBoard platform, which allows researchers to understand our systems in a more fine-grained way.

SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization

Static word embeddings that represent words by a single vector 
cannot capture the variability of word meaning in different linguistic
and extralinguistic contexts. Building on prior work on contextualized 
and dynamic word embeddings, we introduce dynamic contextualized 
word embeddings that represent words as a function of both linguistic
and extralinguistic context. Based on a pretrained language model (PLM), 
dynamic contextualized word embeddings model time and social space 
jointly, which makes them attractive for a range of NLP tasks involving
semantic variability. We highlight potential application scenarios by means 
of qualitative and quantitative analyses on four English datasets.

Dynamic Contextualized Word Embeddings

Named entity recognition (NER) is a fundamental task in natural language processing, these is a long held belief that datasets benefit the model. However, not all the data help with generalization, and some samples may contain ambiguous entities or noisy labels. The existing methods can not distinguish hard samples from noisy samples well, and becomes particularly challenging in the case of overfitting. This paper proposes a new method called Noise-Aware-with-Filter (NAF) to solve the issues from two sides. From the perspective of the data, we design a Logit-Maximum-Difference (LMD) mechanism, which maximizes the diversity between different samples to help the model identify noisy samples. From the perspective of the model, we design an Incomplete-Trust (In-trust) loss function, which boosts $L_{CRF}$ with a robust Distrust-Cross-Entropy(DCE) term. Our proposed In-trust can effectively alleviate the overfitting caused by previous loss function. Experiments on six real-world Chinese and English NER datasets show that NAF outperforms the previous methods, and which obtained the state-of-the-art(SOTA) results on the CoNLL2003 and CoNLL++ datasets.

Named Entity Recognition via Noise Aware Training Mechanism with Data Filter

Pruning has been demonstrated as an effective way of reducing computational complexity for deep networks, especially CNNs for computer vision tasks. In this paper, we investigate the opportunity to accelerate the inference of large-scale pre-trained language model via pruning. We propose EBERT, a dynamic structured pruning algorithm for efficient BERT inference. Unlike previous methods that randomly prune the model weights for static inference, EBERT dynamically determines and prunes the unimportant heads in multi-head self-attention layers and the unimportant structured computations in feed-forward network for each input sample at run-time. Experimental results show that our proposed EBERT outperforms other state-of-the-art methods on different tasks.

EBERT: Efficient BERT Inference with Dynamic Structured Pruning

How to combine several pieces of evidence to verify a claim is an interesting semantic task. Very complex methods have been proposed, combining different evidence vectors using an evidence interaction graph. In this paper, we show that in case of inference based on transformer models, two effective approaches use either (i) a simple application of max pooling over the Transformer evidence vectors; or (ii) computing a weighted sum of the evidence vectors. Our experiments on the FEVER claim verification task show that the methods above achieve the state of the art, constituting strong baseline for much more computationally complex methods.

Strong and Light Baseline Models for Fact-Checking Joint Inference

Data augmentation has recently seen increased interest in NLP due to more work in low-resource domains, new tasks, and the popularity of large-scale neural networks that require large amounts of training data. Despite this recent upsurge, this area is still relatively underexplored, perhaps due to the challenges posed by the discrete nature of language data. In this paper, we present a comprehensive and unifying survey of data augmentation for NLP by summarizing the literature in a structured manner. We first introduce and motivate data augmentation for NLP, and then discuss major methodologically representative approaches. Next, we highlight techniques that are used for popular NLP applications and tasks. We conclude by outlining current challenges and directions for future research. Overall, our paper aims to clarify the landscape of existing literature in data augmentation for NLP and motivate additional work in this area. We also present a GitHub repository with a paper list that will be continuously updated at https://github.com/styfeng/DataAug4NLP

A Survey of Data Augmentation Approaches for NLP

Language model pre-training based on large corpora has achieved tremendous success in terms of constructing enriched contextual representations and has led to significant performance gains on a diverse range of Natural Language Understanding (NLU) tasks. Despite the success, most current pre-trained language models, such as BERT, are trained based on single-grained tokenization, usually with fine-grained characters or sub-words, making it hard for them to learn the precise meaning of coarse-grained words and phrases. In this paper, we propose a simple yet effective pretraining method named LICHEE to efficiently incorporate multi-grained information of input text. Our method can be applied to various pretrained language models and improve their representation capability. Extensive experiments conducted on CLUE and SuperGLUE demonstrate that our method achieves comprehensive improvements on a wide variety of NLU tasks in both Chinese and English with little extra inference cost incurred, and that our best ensemble model achieves the state-of-the-art performance on CLUE benchmark competition.

LICHEE: Improving Language Model Pre-training with Multi-grained Tokenization

Adapting a model to a handful of personalized data is challenging, especially when it has gigantic parameters, such as a Transformer-based pretrained model. The standard way of fine-tuning all the parameters necessitates storing a huge model for each user. In this work, we introduce a lightweight approach dubbed UserAdapter, which clamps hundred millions of parameters of the Transformer model and optimizes a tiny user-specific vector. We take sentiment analysis as a test bed, and collect datasets of reviews from Yelp and IMDB respectively. Results show that, on both datasets, UserAdapter achieves better accuracy than the standard fine-tuned Transformer-based pre-trained model. More importantly, UserAdapter offers an efficient way to produce a personalized Transformer model with less than 0.5\% parameters added for each user.

UserAdapter: Few-Shot User Learning in Sentiment Analysis

Continuous representations of linguistic structures are an important part of modern natural language processing systems. Despite the diversity, most of the existing log-multilinear embedding models are organized under vector operations. However, these operations can not precisely represent the compositionality of natural language due to a lack of order-preserving properties. In this work, we focus on one of the promising alternatives based on the embedding of documents and words in the rotation group through the generalization of the coupled tensor chain decomposition to the exponential family of the probability distributions. In this model, documents and words are represented as matrices, and n-grams representations are combined from word representations by matrix multiplication. The proposed model is optimized via noise-contrastive estimation. We show empirically that capturing word order and higher-order word interactions allows our model to achieve the best results in several document classification benchmarks.


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Dissecting Generation Modes for Abstractive Summarization Models via Ablation and Attribution

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End-to-End Self-Debiasing Framework for Robust NLU Training