Thailand

Sparse attention has been claimed to increase model interpretability under the assumption that it highlights influential inputs. Yet the attention distribution is typically over representations internal to the model rather than the inputs themselves, suggesting this assumption may not have merit. We build on the recent work exploring the interpretability of attention; we design a set of experiments to help us understand how sparsity affects our ability to use attention as an explainability tool. On three text classification tasks, we verify that only a weak relationship between inputs and co-indexed intermediate representations exists—under sparse attention and otherwise. Further, we do not find any plausible mappings from sparse attention distributions to a sparse set of influential inputs through other avenues. Rather, we observe in this setting that inducing sparsity may make it less plausible that attention can be used as a tool for understanding model behavior.

ACL-IJCNLP 2021

Is Sparse Attention more Interpretable?

model interpretability

sparse attention

model analysis

**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.


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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

We present a simple yet effective approach to build multilingual speech-to-text (ST) translation through efficient transfer learning from a pretrained speech encoder and text decoder. Our key finding is that a minimalistic LNA (LayerNorm and Attention) finetuning can achieve zero-shot crosslingual and cross-modality transfer ability by only finetuning 10~50% of the pretrained parameters. This effectively leverages large pretrained models at low training cost such as wav2vec 2.0 for acoustic modeling, and mBART for multilingual text generation. This sets a new state-of-the-art for 36 translation directions (and surpassing cascaded ST for 26 of them) on the large-scale multilingual ST benchmark CoVoST 2 (+6.4 BLEU on average for En-X directions and +6.7 BLEU for X-En directions). Our approach demonstrates strong zero-shot performance in a many-to-many multilingual model (+5.6 BLEU on average across 28 non-English directions), making it an appealing approach for attaining high-quality speech translation with improved parameter and data efficiency.

Multilingual Speech Translation from Efficient Finetuning of Pretrained Models

Is bias amplified when neural machine translation (NMT) models are optimized for speed and evaluated on generic test sets using BLEU? 
We investigate architectures and techniques commonly used to speed up decoding in Transformer-based models, such as greedy search, quantization, average attention networks (AANs) and shallow decoder models and show their effect on gendered noun translation.
We construct a new gender bias test set, SimpleGEN, based on gendered noun phrases in which there is a single, unambiguous, correct answer. 
While we find minimal overall BLEU degradation as we apply speed optimizations, we observe that gendered noun translation performance degrades at a much faster rate.

Gender bias amplification during Speed-Quality optimization in Neural Machine Translation

State-of-the-art machine translation (MT) systems are typically trained to generate ``standard'' target language; however, many languages have multiple varieties (regional varieties, dialects, sociolects, non-native varieties) that are different from the standard language. Such varieties are often low-resource, and hence do not benefit from contemporary NLP solutions, MT included.
We propose a general framework to rapidly adapt MT systems to generate language varieties that are close to, but different from, the standard target language, using no parallel (source--variety) data. This also includes adaptation of MT systems to low-resource typologically-related target languages.
We experiment with adapting an English--Russian MT system to generate Ukrainian and Belarusian, an English--Norwegian Bokmål system to generate Nynorsk, and an English--Arabic system to generate four Arabic dialects, obtaining significant improvements over competitive baselines.

Machine Translation into Low-resource Language Varieties

Learning contextual text embeddings that represent causal graphs has been useful in improving the performance of downstream tasks like causal treatment effect estimation. However, existing causal embeddings which are trained to predict direct causal links, fail to capture other indirect causal links of the graph, thus leading to spurious correlations in downstream tasks. In this paper, we define the faithfulness property of contextual embeddings to capture geometric distance-based properties of directed acyclic causal graphs. By incorporating these faithfulness properties, we learn text embeddings that are 31.3\% more faithful to human validated causal graphs with about 800K and 200K causal links and achieve 21.1\% better Precision-Recall AUC in a link prediction fine-tuning task. Further, in a crowdsourced causal question-answering task on Yahoo! Answers with questions of the form ``What causes X?'', our faithful embeddings achieved a precision of the first ranked answer (P@1) of 41.07\%, outperforming the existing baseline by 10.2\%.

Learning Faithful Representations of Causal Graphs

Transformer-based language models benefit from conditioning on contexts of hundreds to thousands of previous tokens. What aspects of these contexts contribute to accurate model prediction? We describe a series of experiments that measure usable information by selectively ablating lexical and structural information in transformer language models trained on English Wikipedia. In both mid- and long-range contexts, we find that several extremely destructive context manipulations---including shuffling word order within sentences and deleting all words other than nouns---remove less than 15% of the usable information. Our results suggest that long contexts, but not their detailed syntactic and propositional content, are important for the low perplexity of current transformer language models.

What Context Features Can Transformer Language Models Use?

In this paper, we introduce Integrated Directional Gradients (IDG), a method for attributing importance scores to groups of features, indicating their relevance to the output of a neural network model for a given input. The success of Deep Neural Networks has been attributed to their ability to capture higher level feature interactions. Hence, in the last few years capturing the importance of these feature interactions has received increased prominence in ML interpretability literature. In this paper, we formally define the feature group attribution problem and outline a set of axioms that any intuitive feature group attribution method should satisfy. Earlier, cooperative game theory inspired axiomatic methods only borrowed axioms from solution concepts (such as Shapley value) for individual feature attributions and introduced their own extensions to model interactions. In contrast, our formulation is inspired by axioms satisfied by characteristic functions as well as solution concepts in cooperative game theory literature. We believe that characteristic functions are much better suited to model importance of groups compared to just solution concepts. We demonstrate that our proposed method, IDG, satisfies all the axioms. Using IDG we analyze two state-of-the-art text classifiers on three benchmark datasets for sentiment analysis. Our experiments show that IDG is able to effectively capture semantic interactions in linguistic models via negations and conjunctions.

Integrated Directional Gradients: Feature Interaction Attribution for Neural NLP Models

Generating text from structured inputs, such as meaning representations or RDF triples, has often involved the use of specialized graph-encoding neural networks. However, recent applications of pretrained transformers to linearizations of graph inputs have yielded state-of-the-art generation results on graph-to-text tasks. Here, we explore the ability of these linearized models to encode local graph structures, in particular their invariance to the graph linearization strategy and their ability to reconstruct corrupted inputs. Our findings motivate solutions to enrich the quality of models' implicit graph encodings via scaffolding. Namely, we use graph-denoising objectives implemented in a multi-task text-to-text framework. We find that these denoising scaffolds lead to substantial improvements in downstream generation in low-resource settings.

Promoting Graph Awareness in Linearized Graph-to-Text Generation

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

While word segmentation is a solved problem in many languages, it is still a challenge in continuous-script or low-resource languages.
Like other NLP tasks, word segmentation is domain-dependent, which can be a challenge in low-resource languages like Thai and Urdu since there can be domains with insufficient data.
This investigation proposes a new solution to adapt an existing domain-generic model to a target domain, as well as a data augmentation technique to combat the low-resource problems.
In addition to domain adaptation, we also propose a framework to handle out-of-domain inputs using an ensemble of domain-specific models called Multi-Domain Ensemble (MDE).
To assess the effectiveness of the proposed solutions, we conducted extensive experiments on domain adaptation and out-of-domain scenarios.
Moreover, we also proposed a multiple task dataset for Thai text processing, including word segmentation.
For domain adaptation, we compared our solution to the state-of-the-art Thai word segmentation (TWS) method and obtained improvements from 93.47% to 98.48% at the character level and 84.03% to 96.75% at the word level.
For out-of-domain scenarios, our MDE method significantly outperformed the state-of-the-art TWS and multi-criteria methods.
Furthermore, to demonstrate our method's generalizability, we also applied our MDE framework to other languages, namely Chinese, Japanese, and Urdu, and obtained improvements similar to Thai's.

Handling Cross- and Out-of-Domain Samples in Thai Word Segmentation

Sensor names as alphanumeric strings typically encode their key contextual information such as their function or physical location. We focus here on sensors used in smart building applications. In these applications, sensor names are curated in a building vendor-specific manner using different structures and esoteric vocabularies. Tremendous manual effort is needed to annotate sensor nodes for each building or even to just segment these sensor names into meaningful chunks for intelligent operation of buildings. We propose here a fully automated self-supervised framework, Sensei, that can learn to segment sensor names without any human annotation. We employ a neural language model to capture the underlying structure in sensor names and then induce self-supervision based on information from the language model to build the segmentation model. Extensive experiments on five real-world buildings comprising thousands of sensors demonstrate the superiority of Sensei over baseline methods.

Is Sparse Attention more Interpretable?

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