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Learning disentangled representations of textual data is essential for many natural language tasks such as fair classification, style transfer and sentence generation, among others. The existent dominant approaches in the context of text data {either rely} on training an adversary (discriminator) that aims at making attribute values difficult to be inferred from the latent code {or rely on minimising variational bounds of the mutual information between latent code and the value attribute}. {However, the available methods suffer of the impossibility to provide
a fine-grained control of the degree (or force) of disentanglement.} {In contrast to} {adversarial methods}, which are remarkably simple, although the adversary seems to be performing perfectly well during the training phase, after it is completed a fair amount of information about the undesired attribute still remains. This paper introduces a novel variational upper bound to the mutual information between an attribute and the latent code of an encoder. Our bound aims at controlling the approximation error via the Renyi’s divergence, leading to both better disentangled representations and in particular, a precise control of the desirable degree of disentanglement {than state-of-the-art methods proposed for textual data}. Furthermore, it does not suffer from the degeneracy of other losses in multi-class scenarios. We show the superiority of this method on fair classification and on textual style transfer tasks. Additionally, we provide new insights illustrating various trade-offs in style transfer when attempting to learn disentangled representations and quality of the generated sentence.

ACL-IJCNLP 2021

A Novel Estimator of Mutual Information for Learning to Disentangle Textual Representations

fair classification

style transfer

mutual information

**Welcome to ACL-IJCNLP 2021!**

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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

Despite transformers' impressive accuracy, their computational cost is often prohibitive to use with limited computational resources. Most previous approaches to improve inference efficiency require a separate model for each possible computational budget. In this paper, we extend PoWER-BERT (Goyal et al., 2020) and propose Length-Adaptive Transformer that can be used for various inference scenarios after one-shot training. We train a transformer with LengthDrop, a structural variant of dropout, which stochastically determines a sequence length at each layer. We then conduct a multi-objective evolutionary search to find a length configuration that maximizes the accuracy and minimizes the efficiency metric under any given computational budget. Additionally, we significantly extend the applicability of PoWER-BERT beyond sequence-level classification into token-level classification with Drop-and-Restore process that drops word-vectors temporarily in intermediate layers and restores at the last layer if necessary. We empirically verify the utility of the proposed approach by demonstrating the superior accuracy-efficiency trade-off under various setups, including span-based question answering and text classification. Code is available at https://github.com/clovaai/lengthadaptive-transformer.

Length-Adaptive Transformer: Train Once with Length Drop, Use Anytime with Search

Transformer-based pre-trained language models like BERT, though powerful in many tasks, are expensive in both memory and computation, due to their large number of parameters. Previous works show that some parameters in these models can be pruned away without severe accuracy drop. However, these redundant features contribute to a comprehensive understanding of the training data and removing them weakens the model's representation ability. In this paper, we propose GhostBERT, which generates more features with very cheap operations from the remaining features. In this way, GhostBERT has similar memory and computational cost as the pruned model, but enjoys much larger representation power. The proposed ghost module can also be applied to unpruned BERT models to enhance their performance with negligible additional parameters and computation. Empirical results on the GLUE benchmark on three backbone models (i.e., BERT, RoBERTa and ELECTRA) verify the efficacy of our proposed method.

GhostBERT: Generate More Features with Cheap Operations for BERT

The Lottery Ticket Hypothesis suggests that an over-parametrized network consists of ''lottery tickets'', and training a certain collection of them (i.e., a subnetwork) can match the performance of the full model. In this paper, we study such a collection of tickets, which is referred to as ''winning tickets'', in extremely over-parametrized models, e.g., pre-trained language models. We observe that at certain compression ratios, the generalization performance of the winning tickets can not only match but also exceed that of the full model. In particular, we observe a phase transition phenomenon: As the compression ratio increases, generalization performance of the winning tickets first improves then deteriorates after a certain threshold. We refer to the tickets on the threshold as ''super tickets''. We further show that the phase transition is task and model dependent --- as the model size becomes larger and the training data set becomes smaller, the transition becomes more pronounced. Our experiments on the GLUE benchmark show that the super tickets improve single task fine-tuning by 0.9 points on BERT-base and 1.0 points on BERT-large, in terms of task-average score. We also demonstrate that adaptively sharing the super tickets across tasks benefits multi-task learning.

Super Tickets in Pre-Trained Language Models: From Model Compression to Improving Generalization

Neural topic modeling approach has been attracting much attention recently as it is able to leverage the advantages of both neural networks and probabilistic topic models. Previous works have proposed several models that are based on this framework and obtained impressive experimental results compared to traditional probabilistic models. However, the reported result is not consistent across the works, making them hard for gaining a rigorous assessment of these approaches. This work aims to address this issue by offering an extensive empirical evaluation of typical neural topic models in different aspects using large, diverse datasets as well as a thorough set of metrics. Precisely, we examine the performance of these models in three tasks, namely uncovering cohesive topics, modeling the input documents, and representing them for downstream classification. Our results show that while the neural topic models are better in the first and the third tasks, the traditional probabilistic models are still a strong baseline and are better in the second task in many cases. These findings give us more insights for choosing off-the-shelf topic modeling toolboxes in different contexts, as well as for designing more comprehensive evaluation for neural topic models.

Benchmarking Neural Topic Models: An Empirical Study

Pre-trained language models (e.g., BERT) significantly alleviate two traditional challenging problems for Chinese word segmentation (CWS): segmentation ambiguity and out-of-vocabulary (OOV) words. However, such improvements are usually achieved on traditional benchmark datasets and not close to an important goal of CWS: practicability (i.e., low complexity as a standalone task and high beneficiality to downstream tasks). To make a trade-off between traditional evaluation and practicability for CWS, we propose a semi-supervised neural method via pseudo labels. The neural method consists of a teacher model and a student model, which distills knowledge from unlabeled data to the student model so as to improve both in-domain and out-of-domain CWS. Experiments show that our proposed method can not only keep the practicability of the lightweight student model but also improve the performance of segmentation effectively. We also evaluate a range of heterogeneous neural architectures of CWS on downstream Chinese NLP tasks. Results of further experiments demonstrate that our proposed segmenter is reliable and practical as a preprocessing step of the downstream NLP tasks at the minimum cost.

Enhancing Chinese Word Segmentation via Pseudo Labels for Practicability

Code generation is the task of generating code snippets from input user specifications in natural language. Leveraging the linguistically-motivated hierarchical structure of the input can benefit code generation, especially since the specifications are complex sentences containing multiple variables and operations over various data structures. Moreover, recent advances in Transformer architectures have led to improved performance with tree-to-tree style generation for other seq2seq tasks e.g., machine translation. Hence, we present an empirical analysis of the significance of input parse trees for code generation. We run text-to-tree, linearized tree-to-tree, and structured tree-to-tree models, using constituency-based parse trees as input, where the target is Abstract Syntax Tree (AST) of the code. We evaluate our models on the Python-based code generation dataset CoNaLa and a semantic parsing dataset ATIS. We find that constituency trees encoded using a structure-aware model improve performance for both datasets. We also provide an analysis of those aspects of the input parse trees which are most impactful. For instance, we find that structure-aware encodings are better at modelling inputs with multiple variables and capturing long-range dependencies for code generation.

Analysis of Tree-Structured Architectures for Code Generation

While non-autoregressive (NAR) models are showing great promise for machine translation, their use is limited by their dependence on knowledge distillation from autoregressive models. To address this issue, we seek to understand why distillation is so effective. Prior work suggests that distilled training data is less complex than manual translations. Based on experiments with the Levenshtein Transformer and the Mask-Predict NAR models on the WMT14 German-English task, this paper shows that different types of complexity have different impacts: while reducing lexical diversity and decreasing reordering complexity both help NAR learn better alignment between source and target, and thus improve translation quality, lexical diversity is the main reason why distillation increases model confidence, which affects the calibration of different NAR models differently.

How Does Distilled Data Complexity Impact the Quality and Confidence of Non-Autoregressive Machine Translation?

Representation learning for knowledge graphs (KGs) hasfocused on the problem of answering simple link predic-tion queries. In this work we address the more ambitiouschallenge of predicting the answers of conjunctive querieswith multiple missing entities. We propose BidirectionalQuery Embedding (BIQE), a method that embeds con-junctive queries with models based on bi-directional atten-tion mechanisms. Contrary to prior work, bidirectional self-attention can capture interactions among all the elements ofa query graph. We introduce two new challenging datasetsfor studying conjunctive query inference and conduct experi-ments on several benchmark datasets that demonstrate BIQEsignificantly outperforms state of the art baselines

Answering Complex Queries in Knowledge Graphs with Bidirectional Sequence Encoders

Uncertainty quantification is crucial for building reliable and trustable machine learning systems. We propose to estimate uncertainty in recurrent neural networks (RNNs) via stochastic discrete state transitions over recurrent timesteps. The uncertainty of the model can be quantified by running a prediction several times, each time sampling from the recurrent state transition distribution, leading to potentially different results if the model is uncertain. Alongside uncertainty quantification, our proposed method offers several advantages in different settings. The proposed method can (1) learn deterministic and probabilistic automata from data, (2) learn well-calibrated models on real-world classification tasks, (3) improve the performance of out-of-distribution detection, and (4) control the exploration-exploitation trade-off in reinforcement learning.

Uncertainty Estimation and Calibration with Finite-State Probabilistic RNNs

Current pre-trained language models have lots of knowledge, but a more limited ability to use that knowledge. Bloom's Taxonomy helps educators teach children how to use knowledge
by categorizing comprehension skills, so we use it to analyze and improve the comprehension skills of large pre-trained language models. Our experiments focus on zero-shot question answering, using the taxonomy to provide proximal context that helps the model answer questions by being relevant to those questions. We show targeting context in this manner improves performance across 4 popular common sense question answer datasets.


A Novel Estimator of Mutual Information for Learning to Disentangle Textual Representations

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