Ji Xin


2023

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Operator Selection and Ordering in a Pipeline Approach to Efficiency Optimizations for Transformers
Ji Xin | Raphael Tang | Zhiying Jiang | Yaoliang Yu | Jimmy Lin
Findings of the Association for Computational Linguistics: ACL 2023

There exists a wide variety of efficiency methods for natural language processing (NLP) tasks, such as pruning, distillation, dynamic inference, quantization, etc. From a different perspective, we can consider an efficiency method as an operator applied on a model. Naturally, we may construct a pipeline of operators, i.e., to apply multiple efficiency methods on the model sequentially. In this paper, we study the plausibility of this idea, and more importantly, the commutativity and cumulativeness of efficiency operators. We make two interesting observations from our experiments: (1) The operators are commutative—the order of efficiency methods within the pipeline has little impact on the final results; (2) The operators are also cumulative—the final results of combining several efficiency methods can be estimated by combining the results of individual methods. These observations deepen our understanding of efficiency operators and provide useful guidelines for building them in real-world applications.

2022

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Zero-Shot Dense Retrieval with Momentum Adversarial Domain Invariant Representations
Ji Xin | Chenyan Xiong | Ashwin Srinivasan | Ankita Sharma | Damien Jose | Paul Bennett
Findings of the Association for Computational Linguistics: ACL 2022

Dense retrieval (DR) methods conduct text retrieval by first encoding texts in the embedding space and then matching them by nearest neighbor search. This requires strong locality properties from the representation space, e.g., close allocations of each small group of relevant texts, which are hard to generalize to domains without sufficient training data. In this paper, we aim to improve the generalization ability of DR models from source training domains with rich supervision signals to target domains without any relevance label, in the zero-shot setting. To achieve that, we propose Momentum adversarial Domain Invariant Representation learning (MoDIR), which introduces a momentum method to train a domain classifier that distinguishes source versus target domains, and then adversarially updates the DR encoder to learn domain invariant representations. Our experiments show that MoDIR robustly outperforms its baselines on 10+ ranking datasets collected in the BEIR benchmark in the zero-shot setup, with more than 10% relative gains on datasets with enough sensitivity for DR models’ evaluation. Source code is available at https://github.com/ji-xin/modir.

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Certified Error Control of Candidate Set Pruning for Two-Stage Relevance Ranking
Minghan Li | Xinyu Zhang | Ji Xin | Hongyang Zhang | Jimmy Lin
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

In information retrieval (IR), candidate set pruning has been commonly used to speed up two-stage relevance ranking. However, such an approach lacks accurate error control and often trades accuracy against computational efficiency in an empirical fashion, missing theoretical guarantees. In this paper, we propose the concept of certified error control of candidate set pruning for relevance ranking, which means that the test error after pruning is guaranteed to be controlled under a user-specified threshold with high probability. Both in-domain and out-of-domain experiments show that our method successfully prunes the first-stage retrieved candidate sets to improve the second-stage reranking speed while satisfying the pre-specified accuracy constraints in both settings. For example, on MS MARCO Passage v1, our method reduces the average candidate set size from 1000 to 27, increasing reranking speed by about 37 times, while keeping MRR@10 greater than a pre-specified value of 0.38 with about 90% empirical coverage. In contrast, empirical baselines fail to meet such requirements. Code and data are available at: https://github.com/alexlimh/CEC-Ranking.

2021

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BERxiT: Early Exiting for BERT with Better Fine-Tuning and Extension to Regression
Ji Xin | Raphael Tang | Yaoliang Yu | Jimmy Lin
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

The slow speed of BERT has motivated much research on accelerating its inference, and the early exiting idea has been proposed to make trade-offs between model quality and efficiency. This paper aims to address two weaknesses of previous work: (1) existing fine-tuning strategies for early exiting models fail to take full advantage of BERT; (2) methods to make exiting decisions are limited to classification tasks. We propose a more advanced fine-tuning strategy and a learning-to-exit module that extends early exiting to tasks other than classification. Experiments demonstrate improved early exiting for BERT, with better trade-offs obtained by the proposed fine-tuning strategy, successful application to regression tasks, and the possibility to combine it with other acceleration methods. Source code can be found at https://github.com/castorini/berxit.

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How Does BERT Rerank Passages? An Attribution Analysis with Information Bottlenecks
Zhiying Jiang | Raphael Tang | Ji Xin | Jimmy Lin
Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP

Fine-tuned pre-trained transformers achieve the state of the art in passage reranking. Unfortunately, how they make their predictions remains vastly unexplained, especially at the end-to-end, input-to-output level. Little known is how tokens, layers, and passages precisely contribute to the final prediction. In this paper, we address this gap by leveraging the recently developed information bottlenecks for attribution (IBA) framework. On BERT-based models for passage reranking, we quantitatively demonstrate the framework’s veracity in extracting attribution maps, from which we perform detailed, token-wise analysis about how predictions are made. Overall, we find that BERT still cares about exact token matching for reranking; the [CLS] token mainly gathers information for predictions at the last layer; top-ranked passages are robust to token removal; and BERT fine-tuned on MSMARCO has positional bias towards the start of the passage.

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Voice Query Auto Completion
Raphael Tang | Karun Kumar | Kendra Chalkley | Ji Xin | Liming Zhang | Wenyan Li | Gefei Yang | Yajie Mao | Junho Shin | Geoffrey Craig Murray | Jimmy Lin
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Query auto completion (QAC) is the task of predicting a search engine user’s final query from their intermediate, incomplete query. In this paper, we extend QAC to the streaming voice search setting, where automatic speech recognition systems produce intermediate transcriptions as users speak. Naively applying existing methods fails because the intermediate transcriptions often don’t form prefixes or even substrings of the final transcription. To address this issue, we propose to condition QAC approaches on intermediate transcriptions to complete voice queries. We evaluate our models on a speech-enabled smart television with real-life voice search traffic, finding that this ASR-aware conditioning improves the completion quality. Our best method obtains an 18% relative improvement in mean reciprocal rank over previous methods.

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Simple and Effective Unsupervised Redundancy Elimination to Compress Dense Vectors for Passage Retrieval
Xueguang Ma | Minghan Li | Kai Sun | Ji Xin | Jimmy Lin
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Recent work has shown that dense passage retrieval techniques achieve better ranking accuracy in open-domain question answering compared to sparse retrieval techniques such as BM25, but at the cost of large space and memory requirements. In this paper, we analyze the redundancy present in encoded dense vectors and show that the default dimension of 768 is unnecessarily large. To improve space efficiency, we propose a simple unsupervised compression pipeline that consists of principal component analysis (PCA), product quantization, and hybrid search. We further investigate other supervised baselines and find surprisingly that unsupervised PCA outperforms them in some settings. We perform extensive experiments on five question answering datasets and demonstrate that our best pipeline achieves good accuracy–space trade-offs, for example, 48× compression with less than 3% drop in top-100 retrieval accuracy on average or 96× compression with less than 4% drop. Code and data are available at http://pyserini.io/.

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The Art of Abstention: Selective Prediction and Error Regularization for Natural Language Processing
Ji Xin | Raphael Tang | Yaoliang Yu | Jimmy Lin
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

In selective prediction, a classifier is allowed to abstain from making predictions on low-confidence examples. Though this setting is interesting and important, selective prediction has rarely been examined in natural language processing (NLP) tasks. To fill this void in the literature, we study in this paper selective prediction for NLP, comparing different models and confidence estimators. We further propose a simple error regularization trick that improves confidence estimation without substantially increasing the computation budget. We show that recent pre-trained transformer models simultaneously improve both model accuracy and confidence estimation effectiveness. We also find that our proposed regularization improves confidence estimation and can be applied to other relevant scenarios, such as using classifier cascades for accuracy–efficiency trade-offs. Source code for this paper can be found at https://github.com/castorini/transformers-selective.

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Bag-of-Words Baselines for Semantic Code Search
Xinyu Zhang | Ji Xin | Andrew Yates | Jimmy Lin
Proceedings of the 1st Workshop on Natural Language Processing for Programming (NLP4Prog 2021)

The task of semantic code search is to retrieve code snippets from a source code corpus based on an information need expressed in natural language. The semantic gap between natural language and programming languages has for long been regarded as one of the most significant obstacles to the effectiveness of keyword-based information retrieval (IR) methods. It is a common assumption that “traditional” bag-of-words IR methods are poorly suited for semantic code search: our work empirically investigates this assumption. Specifically, we examine the effectiveness of two traditional IR methods, namely BM25 and RM3, on the CodeSearchNet Corpus, which consists of natural language queries paired with relevant code snippets. We find that the two keyword-based methods outperform several pre-BERT neural models. We also compare several code-specific data pre-processing strategies and find that specialized tokenization improves effectiveness.

2020

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Early Exiting BERT for Efficient Document Ranking
Ji Xin | Rodrigo Nogueira | Yaoliang Yu | Jimmy Lin
Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing

Pre-trained language models such as BERT have shown their effectiveness in various tasks. Despite their power, they are known to be computationally intensive, which hinders real-world applications. In this paper, we introduce early exiting BERT for document ranking. With a slight modification, BERT becomes a model with multiple output paths, and each inference sample can exit early from these paths. In this way, computation can be effectively allocated among samples, and overall system latency is significantly reduced while the original quality is maintained. Our experiments on two document ranking datasets demonstrate up to 2.5x inference speedup with minimal quality degradation. The source code of our implementation can be found at https://github.com/castorini/earlyexiting-monobert.

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DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference
Ji Xin | Raphael Tang | Jaejun Lee | Yaoliang Yu | Jimmy Lin
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Large-scale pre-trained language models such as BERT have brought significant improvements to NLP applications. However, they are also notorious for being slow in inference, which makes them difficult to deploy in real-time applications. We propose a simple but effective method, DeeBERT, to accelerate BERT inference. Our approach allows samples to exit earlier without passing through the entire model. Experiments show that DeeBERT is able to save up to ~40% inference time with minimal degradation in model quality. Further analyses show different behaviors in the BERT transformer layers and also reveal their redundancy. Our work provides new ideas to efficiently apply deep transformer-based models to downstream tasks. Code is available at https://github.com/castorini/DeeBERT.

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Showing Your Work Doesn’t Always Work
Raphael Tang | Jaejun Lee | Ji Xin | Xinyu Liu | Yaoliang Yu | Jimmy Lin
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

In natural language processing, a recently popular line of work explores how to best report the experimental results of neural networks. One exemplar publication, titled “Show Your Work: Improved Reporting of Experimental Results” (Dodge et al., 2019), advocates for reporting the expected validation effectiveness of the best-tuned model, with respect to the computational budget. In the present work, we critically examine this paper. As far as statistical generalizability is concerned, we find unspoken pitfalls and caveats with this approach. We analytically show that their estimator is biased and uses error-prone assumptions. We find that the estimator favors negative errors and yields poor bootstrapped confidence intervals. We derive an unbiased alternative and bolster our claims with empirical evidence from statistical simulation. Our codebase is at https://github.com/castorini/meanmax.

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Inserting Information Bottlenecks for Attribution in Transformers
Zhiying Jiang | Raphael Tang | Ji Xin | Jimmy Lin
Findings of the Association for Computational Linguistics: EMNLP 2020

Pretrained transformers achieve the state of the art across tasks in natural language processing, motivating researchers to investigate their inner mechanisms. One common direction is to understand what features are important for prediction. In this paper, we apply information bottlenecks to analyze the attribution of each feature for prediction on a black-box model. We use BERT as the example and evaluate our approach both quantitatively and qualitatively. We show the effectiveness of our method in terms of attribution and the ability to provide insight into how information flows through layers. We demonstrate that our technique outperforms two competitive methods in degradation tests on four datasets. Code is available at https://github.com/bazingagin/IBA.

2019

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What Part of the Neural Network Does This? Understanding LSTMs by Measuring and Dissecting Neurons
Ji Xin | Jimmy Lin | Yaoliang Yu
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Memory neurons of long short-term memory (LSTM) networks encode and process information in powerful yet mysterious ways. While there has been work to analyze their behavior in carrying low-level information such as linguistic properties, how they directly contribute to label prediction remains unclear. We find inspiration from biologists and study the affinity between individual neurons and labels, propose a novel metric to quantify the sensitivity of neurons to each label, and conduct experiments to show the validity of our proposed metric. We discover that some neurons are trained to specialize on a subset of labels, and while dropping an arbitrary neuron has little effect on the overall accuracy of the model, dropping label-specialized neurons predictably and significantly degrades prediction accuracy on the associated label. We further examine the consistency of neuron-label affinity across different models. These observations provide insight into the inner mechanisms of LSTMs.

2018

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Put It Back: Entity Typing with Language Model Enhancement
Ji Xin | Hao Zhu | Xu Han | Zhiyuan Liu | Maosong Sun
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Entity typing aims to classify semantic types of an entity mention in a specific context. Most existing models obtain training data using distant supervision, and inevitably suffer from the problem of noisy labels. To address this issue, we propose entity typing with language model enhancement. It utilizes a language model to measure the compatibility between context sentences and labels, and thereby automatically focuses more on context-dependent labels. Experiments on benchmark datasets demonstrate that our method is capable of enhancing the entity typing model with information from the language model, and significantly outperforms the state-of-the-art baseline. Code and data for this paper can be found from https://github.com/thunlp/LME.