Thailand

Despite recent advances, standard sequence labeling systems often fail when processing noisy user-generated text or consuming the output of an Optical Character Recognition (OCR) process. In this paper, we improve the noise-aware training method by proposing an empirical error generation approach that employs a sequence-to-sequence model trained to perform translation from error-free to erroneous text. Using an OCR engine, we generated a large parallel text corpus for training and produced several real-world noisy sequence labeling benchmarks for evaluation. Moreover, to overcome the data sparsity problem that exacerbates in the case of imperfect textual input, we learned noisy language model-based embeddings. Our approach outperformed the baseline noise generation and error correction techniques on the erroneous sequence labeling data sets. To facilitate future research on robustness, we make our code, embeddings, and data conversion scripts publicly available.

IJCNLP-AACL 2021

Empirical Error Modeling Improves Robustness of Noisy Neural Sequence Labeling

misspellings

sequence-to-sequence models

embeddings

robustness

information extraction

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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).

technical paper

Many joint entity relation extraction models setup two separated label spaces for the two sub-tasks (i.e., entity detection and relation classification). We argue that this setting may hinder the information interaction between entities and relations. In this work, we propose to eliminate the different treatment on the two sub-tasks' label spaces. The input of our model is a table containing all word pairs from a sentence. Entities and relations are represented by squares and rectangles in the table. We apply a unified classifier to predict each cell's label, which unifies the learning of two sub-tasks. For testing, an effective (yet fast) approximate decoder is proposed for finding squares and rectangles from tables. Experiments on three benchmarks (ACE04, ACE05, SciERC) show that, using only half the number of parameters, our model achieves competitive accuracy with the best extractor, and is faster.

UniRE: A Unified Label Space for Entity Relation Extraction

Continual learning has gained increasing attention in recent years, thanks to its biological interpretation and efficiency in many real-world applications. As a typical task of continual learning, continual relation extraction (CRE) aims to extract relations between entities from texts, where the samples of different relations are delivered into the model continuously. Some previous works have proved that storing typical samples of old relations in memory can help the model keep a stable understanding of old relations and avoid forgetting them. However, most methods heavily depend on the memory size in that they simply replay these memorized samples in subsequent tasks. To fully utilize memorized samples, in this paper, we employ relation prototype to extract useful information of each relation. Specifically, the prototype embedding for a specific relation is computed based on memorized samples of this relation, which is collected by K-means algorithm. The prototypes of all observed relations at current learning stage are used to re-initialize a memory network to refine subsequent sample embeddings, which ensures the model's stable understanding on all observed relations when learning a new task. Compared with previous CRE models, our model utilizes the memory information sufficiently and efficiently, resulting in enhanced CRE performance. Our experiments show that the proposed model outperforms the state-of-the-art CRE models and has great advantage in avoiding catastrophic forgetting. The code and datasets are released on https://github.com/fd2014cl/RP-CRE.

Refining Sample Embeddings with Relation Prototypes to Enhance Continual Relation Extraction

Existing multilingual machine translation approaches mainly focus on English-centric directions, while the non-English directions still lag behind. In this work, we aim to build a many-to-many translation system with an emphasis on the quality of non-English language directions. Our intuition is based on the hypothesis that a universal cross-language representation leads to better multilingual translation performance. To this end, we propose mRASP2, a training method to obtain a single unified multilingual translation model. mRASP2 is empowered by two techniques: a) a contrastive learning scheme to close the gap among representations of different languages, and b) data augmentation on both multiple parallel and monolingual data to further align token representations. For English-centric directions, mRASP2 achieves competitive or even better performance than a strong pre-trained model mBART on tens of WMT benchmarks. For non-English directions, mRASP2 achieves an improvement of average 10+ BLEU compared with the multilingual baseline

Contrastive Learning for Many-to-many Multilingual Neural Machine Translation

Neural Machine Translation (NMT) currently exhibits biases such as producing translations that are too short and overgenerating frequent words, and shows poor robustness to copy noise in training data or domain shift. Recent work has tied these shortcomings to beam search -- the de facto standard inference algorithm in NMT -- and Eikema & Aziz (2020) propose to use Minimum Bayes Risk (MBR) decoding on unbiased samples instead.

In this paper, we empirically investigate the properties of MBR decoding on a number of previously reported biases and failure cases of beam search. We find that MBR still exhibits a length and token frequency bias, owing to the MT metrics used as utility functions, but that MBR also increases robustness against copy noise in the training data and domain shift.

Understanding the Properties of Minimum Bayes Risk Decoding in Neural Machine Translation

Since traditional tokenizers are isolated from a downstream task and model, they cannot output an appropriate tokenization depending on the task and model, although recent studies imply that the appropriate tokenization improves the performance.
In this paper, we propose a novel method to find an appropriate tokenization to a given downstream model by jointly optimizing a tokenizer and the model.
The proposed method has no restriction except for using loss values computed by the downstream model to train the tokenizer, and thus, we can apply the proposed method to any NLP task.
Moreover, the proposed method can be used to explore the appropriate tokenization for an already trained model as post-processing.
Therefore, the proposed method is applicable to various situations.
We evaluated whether our method contributes to improving performance on text classification in three languages and machine translation in eight language pairs.
Experimental results show that our proposed method improves the performance by determining appropriate tokenizations.

Joint Optimization of Tokenization and Downstream Model

The attention layer has become a prevalent component in improving the effectiveness of neural network models for NLP tasks. Figuring out why attention is effective and its interpretability has attracted a widespread deliberation. Current studies mostly investigate the effect of attention mechanism based on the attention distribution it generates with one single neural network structure. However they do not consider the changes in semantic capability of different components in the model due to the attention mechanism, which can vary across different network structures. In this paper, we propose a comprehensive analytical framework that exploits a convex hull representation of sequence semantics in an n-dimensional Semantic Euclidean Space and defines a series of indicators to capture the impact of attention on sequence semantics. Through a series of experiments on various NLP tasks and three representative recurrent units, we analyze why and how attention benefits the semantic capacity of different types of recurrent neural networks based on the indicators defined in the proposed framework.

How does Attention Affect the Model?

Recently, chest X-ray report generation, which aims to automatically generate descriptions of given chest X-ray images, has received growing research interests. The key challenge of chest X-ray report generation is to accurately capture and describe the abnormal regions. In most cases, the normal regions dominate the entire chest X-ray image, and the corresponding descriptions of these normal regions dominate the final report. Due to such data bias, learning-based models may fail to attend to abnormal regions. In this work, to effectively capture and describe abnormal regions, we propose the Contrastive Attention (CA) model. Instead of solely focusing on the current input image, the CA model compares the current input image with normal images to distill the contrastive information. The acquired contrastive information can better represent the visual features of abnormal regions. According to the experiments on the public IU-X-ray and MIMIC-CXR datasets, incorporating our CA into several existing models can boost their performance across most metrics. In addition, according to the analysis, the CA model can help existing models better attend to the abnormal regions and provide more accurate descriptions which are crucial for an interpretable diagnosis. Specifically, we achieve the state-of-the-art results on the two public datasets.

Contrastive Attention for Automatic Chest X-ray Report Generation

Video captioning combines video understanding and language generation. Different from image captioning that describes a static image with details of almost every object, video captioning usually considers a sequence of frames and biases towards focused objects, e.g., the objects that stay in focus regardless of the changing background. Therefore, detecting and properly accommodating focused objects is critical in video captioning. To enforce the description of focused objects and achieve controllable video captioning, we propose an Object-Oriented Non-Autoregressive approach (O2NA), which performs caption generation in three steps: 1) identify the focused objects and predict their locations in the target caption; 2) generate the related attribute words and relation words of these focused objects to form a draft caption; and 3) combine video information to refine the draft caption to a fluent final caption. Since the focused objects are generated and located ahead of other words, it is difficult to apply the word-by-word autoregressive generation process; instead, we adopt a non-autoregressive approach. The experiments on two benchmark datasets, i.e., MSR-VTT and MSVD, demonstrate the effectiveness of O2NA, which achieves results competitive with the state-of-the-arts but with both higher diversity and higher inference speed.

O2NA: An Object-Oriented Non-Autoregressive Approach for Controllable Video Captioning

A long-standing challenge in Chinese–English machine translation is that sentence boundaries are ambiguous in Chinese orthography, but inferring good splits is necessary for obtaining high quality translations. To solve this, we use reinforcement learning to train a segmentation policy that splits Chinese texts into segments that can be independently translated so as to maximise the overall translation quality. We compare to a variety of segmentation strategies and find that our approach improves the baseline BLEU score on the WMT2020 Chinese–English news translation task by +0.3 BLEU overall and improves the score on input segments that contain more than 60 words by +3 BLEU.

Better {C}hinese Sentence Segmentation with Reinforcement Learning

Transfer learning has become the dominant paradigm for many natural language processing tasks. In addition to models being pretrained on large datasets, they can be further trained on intermediate (supervised) tasks that are similar to the target task. For small Natural Language Inference (NLI) datasets, language modelling is typically followed by pretraining on a large (labelled) NLI dataset before fine-tuning with each NLI subtask.
In this work, we explore Gradient Boosted Decision Trees (GBDTs) as an alternative to the commonly used Multi-Layer Perceptron (MLP) classification head. GBDTs have desirable properties such as good performance on dense, numerical features and are effective where the ratio of the number of samples w.r.t the number of features is low.
In addition, we introduce FreeGBDT, a method of fitting a GBDT head on the features computed during fine-tuning to increase performance without additional computation by the neural network. We demonstrate the effectiveness of our method on several NLI datasets using a strong baseline model (RoBERTa-large with MNLI pretraining). The FreeGBDT shows a consistent improvement over the standard MLP classification head.

Empirical Error Modeling Improves Robustness of Noisy Neural Sequence Labeling

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