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10.1006/csla.2000.0138 | 2023-05-16 | [
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"table_ref": [... | Previous studies show that intermediate supervision signals benefit various Natural Language Processing tasks. However, it is not clear whether there exist intermediate signals that benefit Neural Machine Translation (NMT). Borrowing techniques from Statistical Machine Translation, we propose intermediate signals which are intermediate sequences from the "source-like" structure to the "target-like" structure. Such intermediate sequences introduce an inductive bias that reflects a domain-agnostic principle of translation, which reduces spurious correlations that are harmful to out-of-domain generalisation. Furthermore, we introduce a full-permutation multi-task learning to alleviate the spurious causal relations from intermediate sequences to the target, which results from exposure bias. The Minimum Bayes Risk decoding algorithm is used to pick the best candidate translation from all permutations to further improve the performance. Experiments show that the introduced intermediate signals can effectively improve the domain robustness of NMT and reduces the amount of hallucinations on outof-domain translation. Further analysis shows that our methods are especially promising in low-resource scenarios. | Progressive Translation: Improving Domain Robustness of Neural Machine Translation with Intermediate Sequences * | [
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"figure_caption": "Figure 1 :1Figure 1: An illustration of the transformation from a source sentence to the target translation and its analogy with vision. src: source; tgt: target; lex: word-by-word translation; ali: reorders lex monotonically based on word alignments.",
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"figure_id... | Chaojun Wang; Yang Liu; Wai Lam | [
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"authors": "Martin Arjovsky; Léon Bottou; Ishaan Gulrajani; David Lopez-Paz",
"journal": "",
"ref_id": "b0",
"title": "Invariant risk minimization",
"year": "2019"
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"authors": "Jean Carletta",
"journal": "Computational Linguistics",
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"title": "Assessing a... | [
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10.33011/lilt.v16i.1417 | 2023-05-16 | [
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"text": "In this research, we ai... | A unique feature of Recurrent Neural Networks (RNNs) is that it incrementally processes input sequences. In this research, we aim to uncover the inherent generalization properties, i.e., inductive bias, of RNNs with respect to how frequently RNNs switch the outputs through time steps in the sequence classification task, which we call output sequence frequency. Previous work analyzed inductive bias by training models with a few synthetic data and comparing the model's generalization with candidate generalization patterns. However, when examining the output sequence frequency, previous methods cannot be directly applied since enumerating candidate patterns is computationally difficult for longer sequences. To this end, we propose to directly calculate the output sequence frequency for each model by regarding the outputs of the model as discrete-time signals and applying frequency domain analysis. Experimental results showed that Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) have an inductive bias towards lowerfrequency patterns, while Elman RNN tends to learn patterns in which the output changes at high frequencies. We also found that the inductive bias of LSTM and GRU varies with the number of layers and the size of hidden layers. | Empirical Analysis of the Inductive Bias of Recurrent Neural Networks by Discrete Fourier Transform of Output Sequences | [
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"figure_caption": "1 Here, k = 1 corresponds to the lowest frequency component and k = N 2 to the highest. One useful measure for analyzing the property of the signal f [n] is the dominant frequency (Ng and Goldberger, 2007). In short, dominant frequency is the frequency component of maximum amplitude and is ... | Taiga Ishii; Ryo Ueda; Yusuke Miyao | [
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"authors": "Jean-Philippe Bernardy",
"journal": "",
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"title": "Can Recurrent Neural Networks Learn Nested Recursion? Linguistic Issues in Language Technology",
"year": "2018"
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"authors": "Kyunghyun Cho; Bart Van Merriënboer; Caglar Gulcehre; Dzmitry Bahdanau; Fethi B... | [
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"formula_text": "F [k] = N -1 n=0 f [n] exp - √ -1 2π N kn . (1) When f [n] is a real-value signal, it is sufficient to consider only k ∈ {1, . . . , N 2 }."
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2023-05-16 | [
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"text": "from itself to guide its own mode... | Knowledge Distillation (KD) is a powerful technique for transferring knowledge between neural network models, where a pre-trained teacher model is used to facilitate the training of the target student model. However, the availability of a suitable teacher model is not always guaranteed. To address this challenge, Self-Knowledge Distillation (SKD) attempts to construct a teacher model from itself. Existing SKD methods add Auxiliary Classifiers (AC) to intermediate layers of the model or use the history models and models with different input data within the same class. However, these methods are computationally expensive and only capture time-wise and classwise features of data. In this paper, we propose a lightweight SKD framework that utilizes multi-source information to construct a more informative teacher. Specifically, we introduce a Distillation with Reverse Guidance (DRG) method that considers different levels of information extracted by the model, including edge, shape, and detail of the input data, to construct a more informative teacher. Additionally, we design a Distillation with Shape-wise Regularization (DSR) method that ensures a consistent shape of ranked model output for all data. We validate the performance of the proposed DRG, DSR, and their combination through comprehensive experiments on various datasets and models. Our results demonstrate the superiority of the proposed methods over baselines (up to 2.87%) and state-of-the-art SKD methods (up to 1.15%), while being computationally efficient and robust. The code is available at https://github.com/xucong-parsifal/LightSKD. | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 1 Lightweight Self-Knowledge Distillation with Multi-source Information Fusion | [
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"figure_caption": "Fig. 1 :1Fig.1: Overview of existing SKD methods, i.e., multi-exit SKD, TW-SKD, and IC-SKD, and our methods, i.e., DRG and DSR.",
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"figure_caption": "Fig. 2 :2Fig. 2: Illustrati... | Xucong Wang; Pengchao Han; Lei Guo | [
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"authors": "G Hinton; O Vinyals; J Dean",
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"ref_id": "b0",
"title": "Distilling the knowledge in a neural network",
"year": "2015"
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"authors": "T Furlanello; Z Lipton; M Tschannen; L Itti; A Anandkumar",
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10.1145/1553374.1553380 | 2023-05-19 | [{"figure_ref":[],"heading":"Introduction","publication_ref":["b2","b1","b32","b23","b41","b8","b35"(...TRUNCATED) | "Information extraction (IE) systems aim to automatically extract structured information, such as na(...TRUNCATED) | Easy-to-Hard Learning for Information Extraction * | [{"figure_caption":"Figure 1 :1Figure1: Overview of E2H consisting of three stages, i.e., the easy s(...TRUNCATED) | Chang Gao; Wenxuan Zhang; Wai Lam; Bing Lidong | [{"authors":"Yoshua Bengio; Jérôme Louradour; Ronan Collobert; Jason Weston","journal":"Associatio(...TRUNCATED) | [{"formula_coordinates":[2.0,306.14,589.38,212.89,15.4],"formula_id":"formula_0","formula_text":"{ ((...TRUNCATED) |
2023-05-16 | [{"figure_ref":["fig_0"],"heading":"Introduction","publication_ref":["b23","b16","b38","b16","b1","b(...TRUNCATED) | "3D LiDAR-based single object tracking (SOT) has gained increasing attention as it plays a crucial r(...TRUNCATED) | Correlation Pyramid Network for 3D Single Object Tracking | [{"figure_caption":"Figure 1 .1Figure 1. Visualization results of the four different categories. The(...TRUNCATED) | Mengmeng Wang; Teli Ma; Xingxing Zuo; Jiajun Lv; Yong Liu | [{"authors":"","journal":"SC3D","ref_id":"b0","title":"","year":""},{"authors":"Luca Bertinetto; Jac(...TRUNCATED) | [{"formula_coordinates":[3.0,50.11,668.33,236.25,33.56],"formula_id":"formula_0","formula_text":"B 1(...TRUNCATED) | |
2024-02-26 | [{"figure_ref":["fig_0"],"heading":"Introduction","publication_ref":["b1","b21","b38","b48","b36","b(...TRUNCATED) | "Iterated belief revision requires information about the current beliefs. This information is repres(...TRUNCATED) | Representing states in iterated belief revision | [{"figure_caption":"Figure 1 :1Figure 1: Comparison of the four considered representations","figure_(...TRUNCATED) | Paolo Liberatore | [{"authors":"C Areces; V Becher","journal":"Springer Science & Business Media","ref_id":"b0","title"(...TRUNCATED) | [{"formula_coordinates":[4.0,242.4,141.4,125.4,124.34],"formula_id":"formula_0","formula_text":"❅ (...TRUNCATED) | |
10.18653/v1/2021.acl-long.224 | 2023-05-22 | [{"figure_ref":["fig_0","fig_0"],"heading":"Introduction","publication_ref":["b9","b3","b4","b15","b(...TRUNCATED) | "We present a new task, speech dialogue translation mediating speakers of different languages. We co(...TRUNCATED) | Towards Speech Dialogue Translation Mediating Speakers of Different Languages | [{"figure_caption":"Figure 1 :1Figure1: The importance of considering context in SDT. \"甘い\" can(...TRUNCATED) | Shuichiro Shimizu; Chenhui Chu; Sheng Li; Sadao Kurohashi | [{"authors":"Luisa Bentivogli; Mauro Cettolo; Marco Gaido; Alina Karakanta; Alberto Martinelli; Matt(...TRUNCATED) | [{"formula_coordinates":[2.0,306.14,285.94,218.27,39.74],"formula_id":"formula_0","formula_text":"er(...TRUNCATED) |
10.1016/j.inffus.2021.05.008 | 2023-07-08 | [{"figure_ref":["fig_7","fig_7","fig_0"],"heading":"Introduction","publication_ref":["b19","b45","b2(...TRUNCATED) | "Generating synthetic data through generative models is gaining interest in the ML community and bey(...TRUNCATED) | Synthetic Data, Real Errors: How (Not) to Publish and Use Synthetic Data | [{"figure_caption":"Figure 2 .2Figure 2. Conclusions drawn from synthetic data do not always transfe(...TRUNCATED) | Boris Van Breugel; Zhaozhi Qian; Mihaela Van Der Schaar | [{"authors":"M Abdar; F Pourpanah; S Hussain; D Rezazadegan; L Liu; M Ghavamzadeh; P Fieguth; X Cao;(...TRUNCATED) | [{"formula_coordinates":[3.0,451.42,102.5,86.52,8.64],"formula_id":"formula_0","formula_text":"(i) ((...TRUNCATED) |
2023-05-16 | [{"figure_ref":[],"heading":"Introduction","publication_ref":["b12","b27","b8","b1","b19","b20","b4"(...TRUNCATED) | "The problem of model counting, also known as #SAT, is to compute the number of models or satisfying(...TRUNCATED) | Rounding Meets Approximate Model Counting | [{"figure_caption":"The number of repetitions depends on max(Pr[L], Pr[U ]). The current algorithmic(...TRUNCATED) | Jiong Yang; Kuldeep S Meel | [{"authors":"R Alur; R Bodik; G Juniwal; M M K Martin; M Raghothaman; S A Seshia; R Singh; A Solar-L(...TRUNCATED) | [{"formula_coordinates":[3.0,134.77,564.29,240.41,14.38],"formula_id":"formula_0","formula_text":"es(...TRUNCATED) | |
10.18653/v1/2020.acl-main.421 | 2023-05-16 | [{"figure_ref":[],"heading":"Introduction","publication_ref":[],"table_ref":[],"text":"Product quest(...TRUNCATED) | "Product Question Answering (PQA) systems are key in e-commerce applications to provide responses to(...TRUNCATED) | xPQA: Cross-Lingual Product Question Answering across 12 Languages | [{"figure_caption":"Figure 2 :2Figure 2: Summary of experimented approaches. The ePQA_MT (and xPQA_M(...TRUNCATED) | Xiaoyu Shen; Akari Asai; Bill Byrne; Adrià De Gispert | [{"authors":"David Adelani; Jesujoba Alabi; Angela Fan; Julia Kreutzer; Xiaoyu Shen; Machel Reid; Da(...TRUNCATED) | [] |
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