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The application of transformer-based contextual representations has became a de facto solution for solving complex NLP tasks. Despite their successes, such representations are arguably opaque as their latent dimensions are not directly interpretable. To alleviate this limitation of contextual representations, we devise such an algorithm where the output representation expresses human-interpretable information of each dimension. 
We achieve this by constructing a transformation matrix based on the semantic content of the embedding space and predefined semantic categories using Hellinger distance.
We evaluate our inferred representations on supersense prediction task. Our experiments reveal that the interpretable nature of transformed contextual representations makes it possible to accurately predict the supersense category of a word by simply looking for its transformed coordinate with the largest coefficient. We quantify the effects of our proposed transformation when applied over traditional dense contextual embeddings. We additionally investigate and report consistent improvements for the integration of sparse contextual word representations into our proposed algorithm.

IJCNLP-AACL 2021

Changing the Basis of Contextual Representations with Explicit Semantics

interpretability

transformers

**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).

workshop paper

Knowledge Graph (KG) completion research usually focuses on densely connected benchmark datasets that are not representative of real KGs. We curate two KG datasets that include biomedical and encyclopedic knowledge and use an existing commonsense KG dataset to explore KG completion in the more realistic setting where dense connectivity is not guaranteed. We develop a deep convolutional network that utilizes textual entity representations and demonstrate that our model outperforms recent KG completion methods in this challenging setting. We find that our model's performance improvements stem primarily from its robustness to sparsity. We then distill the knowledge from the convolutional network into a student network that re-ranks promising candidate entities. This re-ranking stage leads to further improvements in performance and demonstrates the effectiveness of entity re-ranking for KG completion.

Robust Knowledge Graph Completion with Stacked Convolutions and a Student Re-Ranking Network

Computing precise evidences, namely minimal sets of sentences that support or refute a given claim, rather than larger evidences is crucial in fact verification (FV), since larger evidences may contain conflicting pieces some of which support the claim while the other refute, thereby misleading FV. Despite being important, precise evidences are rarely studied by existing methods for FV. It is challenging to find precise evidences due to a large search space with lots of local optimums. Inspired by the strong exploration ability of the deep Q-learning network (DQN), we propose a DQN-based approach to retrieval of precise evidences. In addition, to tackle the label bias on Q-values computed by DQN, we design a post-processing strategy which seeks best thresholds for determining the true labels of computed evidences. Experimental results confirm the effectiveness of DQN in computing precise evidences and demonstrate improvements in achieving accurate claim verification.

A DQN-based Approach to Finding Precise Evidences for Fact Verification

Deployed real-world machine learning applications are often subject to uncontrolled and even potentially malicious inputs. Those out-of-domain inputs can lead to unpredictable outputs and sometimes catastrophic safety issues. 
Prior studies on out-of-domain detection require in-domain task labels and are limited to supervised classification scenarios.
Our work tackles the problem of detecting out-of-domain samples with only unsupervised in-domain data. 
We utilize the latent representations of pre-trained transformers and propose a simple yet effective method to transform features across all layers to construct out-of-domain detectors efficiently. 
Two domain-specific fine-tuning approaches are further proposed to boost detection accuracy.
Our empirical evaluations of related methods on two datasets validate that our method greatly improves out-of-domain detection ability in a more general scenario.

Unsupervised Out-of-Domain Detection via Pre-trained Transformers

The advent of large pre-trained language models has given rise to rapid progress in the field of Natural Language Processing (NLP). While the performance of these models on standard benchmarks has scaled with size, compression techniques such as knowledge distillation have been key in making them practical. We present MATE-KD, a novel text-based adversarial training algorithm which improves the performance of knowledge distillation. MATE-KD first trains a masked language model-based generator to perturb text by maximizing the divergence between teacher and student logits. Then using knowledge distillation a student is trained on both the original and the perturbed training samples. We evaluate our algorithm, using BERT-based models, on the GLUE benchmark and demonstrate that MATE-KD outperforms competitive adversarial learning and data augmentation baselines. On the GLUE test set our 6 layer RoBERTa based model outperforms BERT-large.

MATE-KD: Masked Adversarial TExt, a Companion to Knowledge Distillation

Text simplification reduces the language complexity of professional content for accessibility purposes. End-to-end neural network models have been widely adopted to directly generate the simplified version of input text, usually functioning as a blackbox. We show that
text simplification can be decomposed into a compact pipeline of tasks to ensure the transparency and explainability of the process. The first two steps in this pipeline are often neglected: 1) to predict whether a given piece of text needs to be simplified, and 2) if yes, to identify complex parts of the text. The two tasks can be solved separately using either lexical or deep learning methods, or solved jointly. Simply applying explainable complexity prediction as a preliminary step, the out-of-sample text simplification performance of the state-of-the-art, black-box simplification models can be improved by a large margin.

Explainable Prediction of Text Complexity: The Missing Preliminaries for Text Simplification

In this paper we present our observations and
evaluations by observing the linguistic performance
of the system on several steps on the
training process of various English-to-German
Neural Machine Translation models. The
linguistic performance is measured through
a semi-automatic process using a test suite.
Among several linguistic observations, we find
that the translation quality of some linguistic
categories decreased within the recorded iterations.
Additionally, we notice some drops
of the translation quality of certain categories
when using a larger corpus.

Observing the Learning Curve of Neural Machine Translation with regard to Linguistic Phenomena

Question answering (QA) models for reading comprehension have achieved human-level accuracy on in-distribution test sets.
However, they have been demonstrated to lack robustness to challenge sets, whose distribution is different from that of training sets.
Existing data augmentation methods mitigate this problem by simply augmenting training sets with synthetic examples sampled from the same distribution as the challenge sets.
However, these methods assume that the distribution of a challenge set is known a priori, making them less applicable to unseen challenge sets.
In this study, we focus on question-answer pair generation (QAG) to mitigate this problem.
While most existing QAG methods aim to improve the quality of synthetic examples,
we conjecture that diversity-promoting QAG can mitigate the sparsity of training sets and lead to better robustness.
We present a variational QAG model that generates multiple diverse QA pairs from a paragraph.
Our experiments show that our method can improve the accuracy of 12 challenge sets, as well as the in-distribution accuracy.

Improving the Robustness of QA Models to Challenge Sets with Variational Question-Answer Pair Generation

The quality of the annotated data directly influences in the success of supervised NLP models. However, creating annotated datasets is often time-consuming and expensive. Although the annotation tool takes an important role, we know little about how it influences annotation quality. We compare the quality of annotations for the task of chat-untangling made by non- experts annotators using two different tools. The first is SLATE, an existing command-line based tool, and the second is Parlay, a new tool we developed that integrates mouse interaction and visual links. Our experimental results indicate that, while both tools perform similarly in terms of annotation quality, Parlay offers a significantly better user experience.

Tools Impact on the Quality of Annotations for Chat Untangling

In this study, we investigate the role of the multiple layers in deep transformer models. We design a variant of ALBERT that dynamically adapts the number of layers for each token of the input. The key specificity of ALBERT is that weights are tied across layers. Therefore, the stack of encoder layers iteratively repeats the application of the same transformation function on the input. We interpret the repetition of this application as an iterative process where the token contextualized representations are progressively refined. We analyze this process at the token level during pre-training, fine-tuning, and inference. We show that tokens do not require the same amount of iterations and that difficult or crucial tokens for the task are subject to more iterations.

How Many Layers and Why? An Analysis of the Model Depth in Transformers

Curriculum learning has improved the quality of neural machine translation, where only source-side features are considered in the metrics to determine the difficulty of translation. In this study, we apply curriculum learning to paraphrase generation for the first time. Different from machine translation, paraphrase generation allows a certain level of discrepancy in semantics between source and target, which results in diverse transformations from lexical substitution to reordering of clauses. Hence, the difficulty of transformations requires considering both source and target contexts. We propose an edit distance between a paraphrased sentence pair as a difficulty metric in curriculum learning. Experiments on formality transfer using GYAFC showed that our curriculum learning with edit distance improves the quality of paraphrase generation. Additionally, the proposed method improves the quality of difficult samples, which was not possible for previous methods.

Changing the Basis of Contextual Representations with Explicit Semantics

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