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Large-scale conversational assistants like Alexa, Siri, Cortana and Google Assistant process every utterance using multiple models for domain, intent and named entity recognition. Given the decoupled nature of model development and large traffic volumes, it is extremely difficult to identify utterances processed erroneously by such systems. We address this challenge to detect domain classification errors using offline Transformer models. We leverage N-best domain hypotheses produced by the production system and combine them with the utterance encodings from RoBERTa model. We fine-tune end-to-end in a multi-task setting using a small dataset of human-annotated utterances. We tested our approach for detecting misclassifications from one domain that accounts for &lt;0.5% of the traffic in a large-scale conversational AI system. Our approach achieves an F1 score of 30% outperforming the bi-LSTM baseline by 16.9 points and a strong RoBERTa model by 4.8 points. We improve this further by 2.3 points to 32.3% by ensembling multiple models.

ACL-IJCNLP 2021

Error Detection in Large-Scale Natural Language Understanding Systems Using Transformer Models

alexa

error detection

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.


We were able to keep the registration fees similar to those charged for the virtual ACL 2020. The one fee allows attendance at the main conference and any/all tutorials and workshops. These fees would be $125 Regular Early and $175 Regular Late; $50 Student Early and $75 Student Late. Early registration closes at midnight July 11, 2021 (Eastern Daylight Time).

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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).

Large-scale conversational assistants like Alexa, Siri, Cortana and Google Assistant process every utterance using multiple models for domain, intent and named entity recognition. Given the decoupled nature of model development and large traffic volumes, it is extremely difficult to identify utterances processed erroneously by such systems. We address this challenge to detect domain classification errors using offline Transformer models. We leverage N-best domain hypotheses produced by the production system and combine them with the utterance encodings from RoBERTa model. We fine-tune end-to-end in a multi-task setting using a small dataset of human-annotated utterances. We tested our approach for detecting misclassifications from one domain that accounts for <0.5% of the traffic in a large-scale conversational AI system. Our approach achieves an F1 score of 30% outperforming the bi-LSTM baseline by 16.9 points and a strong RoBERTa model by 4.8 points. We improve this further by 2.3 points to 32.3% by ensembling multiple models.

technical paper

he product reviews summarization task aims to automatically produce a short summary for a set of reviews of a given product. Such summaries are expected to aggregate a range of different opinions in a concise, coherent and informative manner. This challenging task gives rise to two shortcomings in existing work. First, summarizers tend to favor generic content that appears in reviews for many different products, resulting in template-like, less informative summaries. Second, as reviewers often disagree on the pros and cons of a given product, summarizers sometimes yield inconsistent, self-contradicting summaries. We propose the PASS system (Perturb-and-Select Summarizer) that employs a large pre-trained Transformer-based model (T5 in our case), which follows a few-shot fine-tuning scheme. A key component of the PASS system relies on applying systematic perturbations to the model’s input during inference, which allows it to generate multiple different summaries per product. We develop a method for ranking these summaries according to desired criteria, coherence in our case, enabling our system to almost entirely avoid the problem of self-contradiction. We compare our system against strong baselines on publicly available datasets, and show that it produces summaries which are more informative, diverse and coherent.

PASS: Perturb-and-Select Summarizer for Product Reviews

In this paper, we address a novel task, Multiple TimeLine Summarization (MTLS), which extends the flexibility and versatility of Time-Line Summarization (TLS). Given any collection of time-stamped news articles, MTLS automatically discovers important yet different stories and generates a corresponding time-line for each story.To achieve this, we propose a novel unsupervised summarization framework based on two-stage affinity propagation. We also introduce a quantitative evaluation measure for MTLS based on previousTLS evaluation methods. Experimental results show that our MTLS framework demonstrates high effectiveness and MTLS task can give bet-ter results than TLS.

Multi-TimeLine Summarization (MTLS): Improving Timeline Summarization by Generating Multiple Summaries

Recently, opinion summarization, which is the generation of a summary from multiple reviews, has been conducted in a self-supervised manner by considering a sampled review as a pseudo summary. However, non-text data such as image and metadata related to reviews have been considered less often. To use the abundant information contained in non-text data, we propose a self-supervised multimodal opinion summarization framework called MultimodalSum. Our framework obtains a representation of each modality using a separate encoder for each modality, and the text decoder generates a summary. To resolve the inherent heterogeneity of multimodal data, we propose a multimodal training pipeline. We first pretrain the text encoder--decoder based solely on text modality data. Subsequently, we pretrain the non-text modality encoders by considering the pretrained text decoder as a pivot for the homogeneous representation of multimodal data. Finally, to fuse multimodal representations, we train the entire framework in an end-to-end manner. We demonstrate the superiority of MultimodalSum by conducting experiments on Yelp and Amazon datasets.

Self-Supervised Multimodal Opinion Summarization

In recent years, reference-based and supervised summarization evaluation metrics have been widely explored. However, collecting human-annotated references and ratings are costly and time-consuming. To avoid these limitations, we propose a training-free and reference-free summarization evaluation metric. Our metric consists of a centrality-weighted relevance score and a self-referenced redundancy score. The relevance score is computed between the pseudo reference built from the source document and the given summary, where the pseudo reference content is weighted by the sentence centrality to provide importance guidance. Besides an $F_1$-based relevance score, we also design an $F_\beta$-based variant that pays more attention to the recall score. As for the redundancy score of the summary, we compute a self-masked similarity score with the summary itself to evaluate the redundant information in the summary. Finally, we combine the relevance and redundancy scores to produce the final evaluation score of the given summary. Extensive experiments show that our methods can significantly outperform existing methods on both multi-document and single-document summarization evaluation. The source code is released at \href{https://github.com/Chen-Wang-CUHK/Training-Free-and-Ref-Free-Summ-Evaluation}{https://github.com/Chen-Wang-CUHK/Training-Free-and-Ref-Free-Summ-Evaluation}.

A Training-free and Reference-free Summarization Evaluation Metric via Centrality-weighted Relevance and Self-referenced Redundancy

Short textual descriptions of entities provide summaries of their key attributes and have been shown to be useful sources of background knowledge for tasks such as entity linking and question answering. However, generating entity descriptions, especially for new and long-tail entities, can be challenging since relevant information is often scattered across multiple sources with varied content and style. We introduce DESCGEN: given mentions spread over multiple documents, the goal is to generate an entity summary description. DESCGEN consists of 37K entity descriptions from Wikipedia and Fandom, each paired with nine evidence documents on average. The documents were collected using a combination of entity linking and hyperlinks into the entity pages, which together provide high-quality distant supervision. Compared to other multi-document summarization tasks, our task is entity-centric, more abstractive, and covers a wide range of domains. We also propose a two-stage extract-then-generate baseline and show that there exists a large gap (19.9% in ROUGE-L) between state-of-art models and human performance, suggesting that the data will support signiﬁcant future work.

DESCGEN: A Distantly Supervised Datasetfor Generating Entity Descriptions

With the recent success of pre-trained models in NLP, a significant focus was put on interpreting their representations. One of the most prominent approaches is structural probing (Hewitt and Manning, 2019), where a linear projection of word embeddings is performed in order to approximate the topology of dependency structures. In this work, we introduce a new type of structural probing, where the linear projection is decomposed into 1. iso-morphic space rotation; 2. linear scaling that identifies and scales the most relevant dimensions. In addition to syntactic dependency, we evaluate our method on two novel tasks (lexical hypernymy and position in a sentence). We jointly train the probes for multiple tasks and experimentally show that lexical and syntactic information is separated in the representations. Moreover, the orthogonal constraint makes the Structural Probes less vulnerable to memorization.

Introducing Orthogonal Constraint in Structural Probes

Large pretrained models have achieved great success in many natural language processing tasks. However, when they are applied in specific domains, these models suffer from domain shift and bring
challenges in fine-tuning and online serving for latency and capacity constraints. In this paper, we present a general approach to developing small, fast and effective pretrained models for specific domains. This is achieved by adapting the off-the-shelf general pretrained models and performing task-agnostic knowledge distillation in target domains. Specifically, we propose domain-specific vocabulary expansion in the adaptation stage and employ corpus level occurrence probability to choose the size of incremental vocabulary automatically. Then we systematically explore different strategies to compress the large pretrained models for specific domains. We conduct our experiments in the biomedical and computer science domain. The experimental results demonstrate that our approach achieves better performance over the BERT<sub>BASE</sub>model in domain-specific tasks while 3.3× smaller and 5.1× faster than BERT<sub>BASE</sub>.
The code and pretrained models are available at <https://aka.ms/adalm>.

Adapt-and-Distill: Developing Small, Fast and Effective Pretrained Language Models for Domains

Adversarial debiasing can help to learn fairer models. Previous work has assumed that both main task labels and protected attributes are available in the dataset. However, protected labels are often unavailable, or only available in limited numbers. In this paper, we propose a training strategy which needs only a small amount of protected labels in adversarial training, and propose an estimation method to transfer private-labelled instances from one dataset to another. We demonstrate the in- and cross-domain effectiveness of our method through a range of experiments.

Decoupling Adversarial Training for Fair NLP

While neural language models can generate text with remarkable fluency and coherence, controlling for factual correctness in generation remains an open research question. This major discrepancy between the surface-level fluency and the content-level correctness of neural generation has motivated a new line of research that seeks automatic metrics for evaluating the factuality of machine text. In this paper, we introduce GO FIGURE, a meta-evaluation framework for evaluating factuality evaluation metrics. We propose five necessary conditions to evaluate factuality metrics 
on diagnostic factuality data across three different summarization tasks. Our benchmark analysis on ten factuality metrics reveals that our meta-evaluation framework provides a robust and efficient evaluation that is extensible to multiple types of factual consistency and standard generation metrics, including QA metrics. It also reveals that while QA metrics generally improve over standard metrics that measure factuality across domains, performance is highly dependent on the way in which questions are generated.

GO FIGURE: A Meta Evaluation of Factuality in Summarization

Recent work has shown that Aspect-Term Sentiment Analysis (ATSA) can be effectively performed by Gradual Machine Learning (GML), which begins with some automatically labeled easy instances, and then gradually labels more challenging instances by iterative factor graph inference without manual intervention. In GML, shared features serve as the medium for knowledge conveyance between unlabeled and labeled instances. However, the existing GML solution extracts features based on lexicons, which may not be accurate nor complete and thus lead to insufficient knowledge conveyance.

In this paper, we propose a Deep Neural Network (DNN) driven GML approach for ATSA, which can effectively leverage the strength of DNN in feature representation. It first uses an unsupervised neural network to cluster the automatically extracted features by their sentiment polarities. Then, it models the clustered features as factors to enable implicit knowledge conveyance for gradual inference in a factor graph. Furthermore, provided with labeled training data, we present a hybrid solution that similarly enables gradual learning by fusing the influence of DNN predictions and implicit knowledge conveyance. Finally, we have empirically evaluated the performance of the proposed approach on real benchmark data. Our extensive experiments have shown that the proposed approach consistently achieves the state-of-the-art performance across all the test datasets in both unsupervised and supervised settings and the improvement margins are considerable.

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