Thailand

In this paper we explore the improvement of intent recognition in conversational systems by the use of meta-knowledge embedded in intent identifiers. Developers often include such knowledge, structure as taxonomies, in the documentation of chatbots. By using neuro-symbolic algorithms to incorporate those taxonomies into embeddings of the output space, we were able to improve accuracy in intent recognition. In datasets with intents and example utterances from 200 professional chatbots, we saw decreases in the equal error rate (EER) in more than 40% of the chatbots in comparison to the baseline of the same algorithm without the meta-knowledge. The meta-knowledge proved also to be effective in detecting out-of-scope utterances, improving the false acceptance rate (FAR) in two thirds of the chatbots, with decreases of 0.05 or more in FAR in almost 40% of the chatbots. When considering only the well-developed workspaces with a high level use of taxonomies, FAR decreased more than 0.05 in 77% of them, and more than 0.1 in 39% of the chatbots.

ACL-IJCNLP 2021

Using Meta-Knowledge Mined from Identifiers to Improve Intent Recognition in Conversational Systems

intent classification.

neuro-symbolic algorithms

conversational systems

**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

Transformer-based language models have taken many fields in NLP by storm. BERT and its derivatives dominate most of the existing evaluation benchmarks, including those for Word Sense Disambiguation (WSD), thanks to their ability in capturing context-sensitive semantic nuances. However, there is still little knowledge about their capabilities and potential limitations in encoding and recovering word senses. In this article, we provide an in-depth quantitative and qualitative analysis of the celebrated BERT model with respect to lexical ambiguity. One of the main conclusions of our analysis is that BERT can accurately capture high-level sense distinctions, even when a limited number of examples is available for each word sense. Our analysis also reveals that in some cases language models come close to solving coarse-grained noun disambiguation under ideal conditions in terms of availability of training data and computing resources. However, this scenario rarely occurs in real-world settings and, hence, many practical challenges remain even in the coarse-grained setting. We also perform an in-depth comparison of the two main language model based WSD strategies, namely, fine-tuning and feature extraction, finding that the latter approach is more robust with respect to sense bias and it can better exploit limited available training data. In fact, the simple feature extraction strategy of averaging contextualized embeddings proves robust even using only three training sentences per word sense, with minimal improvements obtained by increasing the size of this training data.

Analysis and Evaluation of Language Models for Word Sense Disambiguation

Task-oriented dialogue systems typically rely on large amounts of high-quality training data or require complex handcrafted rules. However, existing datasets are often limited in size considering the complexity of the dialogues. Additionally, conventional training signal inference does not support non-deterministic agent behaviour, i.e. agents considering multiple actions as valid in identical dialogue states. We propose the Conversation Graph (ConvGraph), a graphbased representation of dialogues that can be exploited for data augmentation, and
multi-reference training and evaluation of non-deterministic agents. ConvGraph generates novel dialogue paths to augment data volume and diversity. Intrinsic and extrinsic evaluation across three datasets shows that data augmentation and multi-reference training with ConvGraph can improve dialogue success rates by up to 6.4%.

Conversation Graph: Data Augmentation, Training and Evaluation for Non-Deterministic Dialogue Management

Conventional autoregressive models have achieved great success in text generation but suffer from the exposure bias problem in that token sequences in the training and in the generation stages are mismatched.
While generative adversarial networks (GANs) can remedy this problem, existing implementations of GANs directly on the discrete outputs tend to be unstable and lack diversity.
In this work, we propose TILGAN, a Transformer-based Implicit Latent GAN, which combines a Transformer autoencoder and GAN in the latent space with a novel design and distribution matching based on the Kullback-Leibler (KL) divergence.
Specifically, to improve the local and global coherence, we explicitly introduce a multi-scale discriminator to capture the semantic information at varying scales among the sequence of hidden representations encoded by Transformer. 
Moreover, the decoder is enhanced by an additional KL loss to be consistent with the latent-generator. 
Experimental results on three benchmark datasets demonstrate the validity and effectiveness of our model, by obtaining significant improvements and a better quality-diversity trade-off in automatic and human evaluation for both unconditional and conditional generation tasks. Our code is available at https://github.com/shizhediao/TILGAN.

TILGAN: Transformer-based Implicit Latent GAN for Diverse and Coherent Text Generation

For many (minority) languages, the resources needed to train large models are not available. We investigate the performance of zero-shot transfer learning with as little data as possible, and the influence of language similarity in this process. We retrain the lexical layers of four BERT-based models using data from two low-resource target language varieties, while the Transformer layers are independently fine-tuned on a POS-tagging task in the model's source language. By combining the new lexical layers and fine-tuned Transformer layers, we achieve high task performance for both target languages. With high language similarity, 10MB of data appears sufficient to achieve substantial monolingual transfer performance. Monolingual BERT-based models generally achieve higher downstream task performance after retraining the lexical layer than multilingual BERT, even when the target language is included in the multilingual model.

Adapting Monolingual Models: Data can be Scarce when Language Similarity is High

We consider a joint information extraction (IE) model, solving named entity recognition, coreference resolution and relation extraction jointly over the whole document. In particular, we study how to inject information from a knowledge base (KB) in such IE model, based on unsupervised entity linking. The used KB entity representations are learned from either (i) hyperlinked text documents (Wikipedia), or (ii) a knowledge graph (Wikidata), and appear complementary in raising IE performance. Representations of corresponding entity linking (EL) candidates are added to text span representations of the input document, and we experiment with (i) taking a weighted average of the EL candidate representations based on their prior (in Wikipedia), and (ii) using an attention scheme over the EL candidate list. Results demonstrate an increase of up to 5% F1-score for the evaluated IE tasks on two datasets. Despite a strong performance of the prior-based model, our quantitative and qualitative analysis reveals the advantage of using the attention-based approach.

Injecting Knowledge Base Information into End-to-End Joint Entity and Relation Extraction and Coreference Resolution

Though being a primary trend for enhancing interpretability of neural networks, attention mechanism's reliability and validity are still under debate. In this paper, we try to purify attention scores to obtain a more faithful explanation of downstream models. Specifically, we propose a framework consisting of a learner and a compressor, which performs fine-tuning and compressing iteratively to enhance the performance and interpretability of the attention mechanism. The learner focuses on learning better text representations to achieve good decisions by fine-tuning, while the compressor aims to perform compressions over the representations to retain the most useful clues for explanations with a Variational information bottleneck ATtention (VAT) mechanism. Extensive experiments on eight benchmark datasets show the great advantages of our proposed approach in terms of both performance and interpretability.

Attending via both Fine-tuning and Compressing

Pre-trained transformer language models have shown remarkable performance on a variety of NLP tasks. However, recent research has suggested that phrase-level representations in these models reflect heavy influences of lexical content, but lack evidence of sophisticated, compositional phrase information. Here we investigate the impact of fine-tuning on the capacity of contextualized embeddings to capture phrase meaning information beyond lexical content. Specifically, we fine-tune models on an adversarial paraphrase classification task with high lexical overlap, and on a sentiment classification task. After fine-tuning, we analyze phrasal representations in controlled settings following prior work. We find that fine-tuning largely fails to benefit compositionality in these representations, though training on sentiment yields a small, localized benefit for certain models. In follow-up analyses, we identify confounding cues in the paraphrase dataset that may explain the lack of composition benefits from that task, and we discuss potential factors underlying the localized benefits from sentiment training.

On the Interplay Between Fine-tuning and Composition in Transformers

Incorporating attribute information such as user and product features into deep neural networks has been shown to be useful in sentiment analysis. Previous works typically accomplished this in two ways: concatenating multiple attributes to word/text representation or treating them as a bias to adjust attention distribution. To leverage the advantages of both methods, this paper proposes a multi-attribute BERT (MA-BERT) to incorporate external attribute knowledge. The proposed method has two advantages. First, it applies multi-attribute transformer (MA-Transformer) encoders to incorporate multiple attributes into both input representation and attention distribution. Second, the MA-Transformer is implemented as a universal layer and stacked on a BERT-based model such that it can be initialized from a pre-trained checkpoint and fine-tuned for the downstream applications without extra pre-training costs. Experiments on three benchmark datasets show that the proposed method outperformed pre-trained BERT models and other methods incorporating external attribute knowledge.

MA-BERT: Learning Representation by Incorporating Multi-Attribute Knowledge in Transformers

Ever since the advent of deep learning, cross-modal representation learning has been dominated by the approaches involving convolutional neural networks for visual representation and recurrent neural networks for language representation. Transformer architecture, however, has rapidly taken over the recurrent neural networks in natural language processing tasks, and it has also been shown that vision tasks can be handled with transformer architecture, with compatible performance to convolutional neural networks. Such results naturally lead to speculation upon the possibility of tackling cross-modal representation for vision and language exclusively with transformer. This paper examines transformer-exclusive cross-modal representation to explore such possibility, demonstrating its potentials as well as discussing its current limitations and its prospects.

Transformer-Exclusive Cross-Modal Representation for Vision and Language

Recent studies on the analysis of the multilingual representations focus on identifying whether there is an emergence of language-independent representations, or whether a multilingual model partitions its weights among different languages. While most of such work has been conducted in a "black-box" manner, this paper aims to analyze individual components of a multilingual neural translation (NMT) model. In particular, we look at the encoder self-attention and encoder-decoder attention heads (in a many-to-one NMT model) that are more specific to the translation of a certain language pair than others by (1) employing metrics that quantify some aspects of the attention weights such as "variance" or "confidence", and (2) systematically ranking the importance of attention heads with respect to translation quality. Experimental results show that surprisingly, the set of most important attention heads are very similar across the language pairs and that it is possible to remove nearly one-third of the less important heads without hurting the translation quality greatly.

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