United Arab Emirates

One of the major challenges of training Natural Language Understanding (NLU) production models lies in the discrepancy between the distributions of the offline training data and of the online live data, due to, e.g., biased sampling scheme, cyclic seasonality shifts, annotated training data coming from a variety of different sources, and a changing pool of users. Consequently, the model trained by the offline data is biased. We often observe this problem especially in task-oriented conversational systems, where topics of interest and the characteristics of users using the system change over time. In this paper we propose an unsupervised approach to mitigate the offline training data sampling bias in multiple NLU tasks. We show that a local distribution approximation in the pre-trained embedding space enables the estimation of importance weights for training samples guiding re-sampling for an effective bias mitigation. We illustrate our novel approach using multiple NLU datasets and show improvements obtained without additional annotation, making this a general approach for mitigating effects of sampling bias.

EMNLP 2022

Unsupervised training data re-weighting for natural language understanding with local distribution approximation

nlu shift unsupervised

## Welcome to NAACL 2022!
The Annual Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL) is taking place July 10-15, 2022 as a hybrid event, in Seattle, WA and online. We are happy to welcome all of you to conference! 
 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, keynote presentations, a plenary panel on the place of linguistics and symbolic structures in NLP, and an Industrial Track panel on Careers in NLP.
 
Posters (including Findings of NAACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop and will have an oral session and a poster session.
 
The program also features 6 Tutorials and 26 Workshops.
 
 We wish you a wonderful conference!
 
[The NAACL 2022 Organizing Committee](https://2022.naacl.org/committees/organization/)

NAACL 2022

2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

poster

## Welcome to EMNLP 2022!
I am delighted to welcome you to EMNLP 2022! I believe this conference has been complicated beyond any precedent. Over the past year, it’s been thrilling to see the organization team approach each new puzzle with creativity and enthusiasm. We hope that those participating in Abu Dhabi as well as those joining remotely will leave the conference feeling newly inspired by the program and newly connected to our ever-growing community. Following EMNLP 2021 and major NLP conferences since, EMNLP 2022 is “hybrid,” serving both virtual and in-person participants.

Our key innovations for EMNLP 2022 include:

* EMNLP 2022 is “hybrid” in a second sense, as well: we allowed both direct and rolling review paper submissions, building on the pilot experiment of EMNLP 2021, which considered a small number of ARR submissions. 
* Familiar from NAACL but new to EMNLP, we’ve added an industry track.
* During the conference, “portals” will link virtual poster sessions to in-person conference participants during poster sessions each day.
* The first ACL-family conference in the United Arab Emirates.

 *Message from Noah A. Smith, University of Washington and Allen Institute for AI, Seattle, Washington, USA* 
***EMNLP 2022 General Chair***
 
[![](https://assets.underline.io/uploads/markdown_image/1/image/9eec7d4a287ee18c278b08229290aa83.png)](https://drive.google.com/file/d/1OlPv6QBeo62VVTughj2jkiLeyHd1WnUt/view)
 
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Welcome!
EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

Counselor reflection is a core verbal skill used by mental health counselors to express understanding and affirmation of the client's experience and concerns. In this paper, we propose a system for the analysis of counselor reflections. Specifically, our system takes as input one dialog turn containing a client prompt and a counselor response, and outputs a score indicating the level of reflection in the counselor response. We compile a dataset consisting of different levels of reflective listening skills, and propose the Prompt-Aware margIn Ranking (PAIR) framework that contrasts positive and negative prompt and response pairs using specially designed multi-gap and prompt-aware margin ranking losses. Through empirical evaluations and deployment of our system in a real-life educational environment, we show that our analysis model outperforms several baselines on different metrics, and can be used to provide useful feedback to counseling trainees.

PAIR: Prompt-Aware margIn Ranking for Counselor Reflection Scoring in Motivational Interviewing

The predictions of question answering (QA) systems are typically evaluated against manually annotated finite sets of one or more answers. This leads to a coverage limitation that results in underestimating the true performance of systems, and is typically addressed by extending over exact match (EM) with predefined rules or with the token-level F1 measure.
In this paper, we present the first systematic conceptual and data-driven analysis to examine the shortcomings of token-level equivalence measures.

To this end, we define the asymmetric notion of answer equivalence (AE), accepting answers that are equivalent to or improve over the reference, and publish over 23k human judgements for candidates produced by multiple QA systems on SQuAD.

Through a careful analysis of this data, we reveal and quantify several concrete limitations of the F1 measure, such as a false impression of graduality, or missing dependence on the question.

Since collecting AE annotations for each evaluated model is expensive, we learn a BERT matching (BEM) measure to approximate this task. Being a simpler task than QA, we find BEM to provide significantly better AE approximations than F1, and to more accurately reflect the performance of systems.

Finally, we demonstrate the practical utility of AE and BEM on the concrete application of minimal accurate prediction sets, reducing the number of required answers by up to X2.6.

Tomayto, Tomahto. Beyond Token-level Answer Equivalence for Question Answering Evaluation

Efficient transformer variants with linear time complexity have been developed to mitigate the quadratic computational overhead of the vanilla transformer. Among them are low-rank projection methods such as Linformer and kernel-based Transformers. Despite their unique merits, they usually suffer from a performance drop comparing with the vanilla transformer on many sequence generation tasks, and often fail to obtain computation gain when the generation is short. We propose Memsizer, an approach towards closing the performance gap while improving the efficiency even with short generation. It projects the source sequences into lower dimension representations like Linformer, while enjoying efficient recurrent-style incremental computation similar to kernel-based transformers. This yields linear computation time and constant memory complexity at inference time. Memsizer also employs a lightweight multi-head mechanism which renders the computation as light as a single-head model. We demonstrate that Memsizer provides an improved balance between efficiency and accuracy over the vanilla transformer and other efficient transformer variants in three typical sequence generation tasks, including machine translation, abstractive text summarization, and language modeling.

Linearizing Transformer with Key-Value Memory

As multimodal learning finds applications in a wide variety of high-stakes societal tasks, investigating their robustness becomes important. Existing work has focused on understanding the robustness of vision-and-language models to imperceptible variations on benchmark tasks. In this work, we investigate the robustness of multimodal classifiers to cross-modal dilutions – a plausible variation. We develop a model that, given a multimodal (image + text) input, generates additional dilution text that (a) maintains relevance and topical coherence with the image and existing text, and (b) when added to the original text, leads to misclassification of the multimodal input. Via experiments on Crisis Humanitarianism and Sentiment Detection tasks, we find that the performance of task-specific fusion-based multimodal classifiers drops by 23.3% and 22.5%, respectively, in the presence of dilutions generated by our model. Metric-based comparisons with several baselines and human evaluations indicate that our dilutions show higher relevance and topical coherence, while simultaneously being more effective at demonstrating the brittleness of the multimodal classifiers. Our work aims to highlight and encourage further research on the robustness of deep multimodal models to realistic variations, especially in human-facing societal applications.

Robustness of Fusion-based Multimodal Classifiers to Cross-Modal Content Dilutions

Progress in summarizing long texts is inhibited by the lack of appropriate evaluation frameworks. A long summary that appropriately covers the facets of that text must also present a coherent narrative, but current automatic and human evaluation methods fail to identify gaps in coherence. In this work, we introduce SNaC, a narrative coherence evaluation framework for fine-grained annotations of long summaries. We develop a taxonomy of coherence errors in generated narrative summaries and collect span-level annotations for 6.6k sentences across 150 book and movie summaries. Our work provides the first characterization of coherence errors generated by state-of-the-art summarization models and a protocol for eliciting coherence judgments from crowdworkers. Furthermore, we show that the collected annotations allow us to benchmark past work in coherence modeling and train a strong classifier for automatically localizing coherence errors in generated summaries. Finally, our SNaC framework can support future work in long document summarization and coherence evaluation, including improved summarization modeling and post-hoc summary correction.

SNaC: Coherence Error Detection for Narrative Summarization

Summarization systems make numerous ``decisions'' about summary properties during inference, e.g. degree of copying, specificity and length of outputs, etc. However, these are implicitly encoded within model parameters and specific styles cannot be enforced. To address this, we introduce HydraSum, a new summarization architecture that extends the single decoder framework of current models to a mixture-of-experts version with multiple decoders. We show that HydraSum's multiple decoders automatically learn contrasting summary styles when trained under the standard training objective without any extra supervision. Through experiments on three summarization datasets (CNN, Newsroom and XSum), we show that HydraSum provides a simple mechanism to obtain stylistically-diverse summaries by sampling from either individual decoders or their mixtures, outperforming baseline models. Finally, we demonstrate that a small modification to the gating strategy during training can enforce an even stricter style partitioning, e.g. high- vs low-abstractiveness or high- vs low-specificity, allowing users to sample from a larger area in the generation space and vary summary styles along multiple dimensions.

HydraSum: Disentangling Style Features in Text Summarization with Multi-Decoder Models

Reasoning over natural language is a long-standing goal for the research community. However, studies have shown that existing language models are inadequate in reasoning. To address the issue, we present POET, a novel reasoning pre-training paradigm. Through pre-training language models with programs and their execution results, POET empowers language models to harvest the reasoning knowledge possessed by program executors via a data-driven approach. POET is conceptually simple and can be instantiated by different kinds of program executors. In this paper, we showcase two simple instances POET-Math and POET-Logic, in addition to a complex instance, POET-SQL. Experimental results on six benchmarks demonstrate that POET can significantly boost model performance in natural language reasoning, such as numerical reasoning, logical reasoning, and multi-hop reasoning. POET opens a new gate on reasoning-enhancement pre-training, and we hope our analysis would shed light on the future research of reasoning like program executors.

Reasoning Like Program Executors

In productionized machine learning systems, online model performance is known to deteriorate over time when there is a distributional drift between offline training and online application data. 
As a remedy, models are typically retrained at fixed time intervals, implying high computational and manual costs. This work aims at decreasing such costs in productionized, large-scale Spoken Language Understanding systems. In particular, we develop a need-based re-training strategy guided by an efficient drift detector and discuss the arising challenges including system complexity, overlapping model releases, observation limitation and the absence of annotated resources at runtime. We present empirical results on historical data and confirm the utility of our design decisions via an online A/B experiment.

Towards Need-Based Spoken Language Understanding Model Updates: What Have We Learned?

Matching a seller listed item to an appropriate product is an important step for an e-commerce platform. With the recent advancement in deep learning, there are different encoder based approaches being proposed as solution. When textual data for two products are available, cross-encoder approaches encode them jointly while bi-encoder approaches encode them separately. Since cross-encoders are computationally heavy, approaches based on bi-encoders are a common practice for this challenge. In this paper, we propose cross-encoder data annotation; a technique to annotate or refine human annotated training data for bi-encoder models using a cross-encoder model. This technique enables us to build a robust model without annotation on newly collected training data or further improve model performance on annotated training data. We evaluate the cross-encoder data annotation on the product matching task using a real-world e-commerce dataset containing 104 million products. Experimental results show that the cross-encoder data annotation improves 4% absolute accuracy when no annotation for training data is available, and 2% absolute accuracy when annotation for training data is available.

Cross-Encoder Data Annotation for Bi-Encoder Based Product Matching

Business-specific Frequently Asked Questions (FAQ) retrieval in task-oriented dialog systems poses unique challenges vis à vis community based FAQs. Each FAQ question represents an intent which is usually an umbrella term for many related user queries. We evaluate performance for such Business FAQs both with standard FAQ retrieval techniques using query-Question (q-Q) similarity and few-shot intent detection techniques. Implementing a real-world solution for FAQ retrieval in order to support multiple tenants (FAQ sets) entails optimizing speed, accuracy and cost. We propose a novel approach to scale multi-tenant FAQ applications in real-world context by contrastive fine-tuning of the last layer in sentence Bi-Encoders along with tenant-specific weight switching.

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Next from EMNLP 2022

PAIR: Prompt-Aware margIn Ranking for Counselor Reflection Scoring in Motivational Interviewing

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