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Query rewriting (QR) is an important technique for user friction (i.e. recovering ASR error or system error) reduction and contextual carryover (i.e. ellipsis and co-reference) in conversational AI systems. Recently, generation-based QR models have achieved promising results on these two tasks separately. Although these two tasks have many similarities such as they both use the previous dialogue along with the current request as model input, there is no unified model to solve them jointly. To this end, we propose a unified contextual query rewriting model that unifies QR for both reducing friction and contextual carryover purpose. Moreover, we involve multiple auxiliary tasks such as trigger prediction and NLU interpretation tasks to boost the performance of the rewrite. We leverage the text-to-text unified framework which uses independent tasks with weighted loss to account for task importance. Then we propose new unified multitask learning strategies including a sequential model which outputs one sentence for multi-tasks, and a hybrid model where some tasks are independent and some tasks are sequentially generated. Our experimental results demonstrate the effectiveness of the proposed unified learning methods.

ACL 2023

Unified Contextual Query Rewriting

contextual query rewrite

unified learning

generative model

poster

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
<br>The conference will be held in Toronto, Canada, July 9-14, 2023.<br><br>
Following the succession of the recent conferences in our field, ACL 2023 will adopt a hybrid format.
While the impact of Covid has considerably diminished in terms of traveling, obtaining visas to Canada
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so everyone has the opportunity to interact with other participants and enjoy the conference. Based on the
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you join us in person or virtually, we sincerely hope everyone has a remarkable conference experience.<br>
This General Chair’s message is where I express my gratitude to the many individuals who have made
enormous contributions to the conference over the past year.

Read [**ACL 2023 General Chair's message**](https://docs.google.com/document/d/1WobYM7norbG4dI48s75HfJoD89qgX5a_F-6U8AteLSA/edit?usp=sharing/) in full.

##### **[Conference Handbook](https://2023.aclweb.org/downloads/acl2023-handbook.pdf)**
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The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing.

Conceptual metaphors present a powerful cognitive vehicle to transfer knowledge structures from a source to a target domain. Prior neural approaches focus on detecting whether natural language sequences are metaphoric or literal. We believe that to truly probe metaphoric knowledge in pre-trained language models, their capability to detect this transfer should be investigated. To this end, this paper proposes to probe the ability of GPT-3 to detect metaphoric language and predict the metaphor's source domain without any pre-set domains. We experiment with different training sample configurations for fine-tuning and few-shot prompting on two distinct datasets. When provided 12 few-shot samples in the prompt, GPT-3 generates the correct source domain for a new sample with an accuracy of 65.15% in English and 34.65% in Spanish. GPT's most common error is a hallucinated source domain for which no indicator is present in the sentence. Other common errors include identifying a sequence as literal even though a metaphor is present and predicting the wrong source domain based on specific words in the sequence that are not metaphorically related to the target domain.

<iframe src="https://app.sli.do/event/eX4PwSmcCc1EvCqjxcYdKB/embed/polls/7f67206c-f9df-4ba7-97b1-6c8d50c60ca5" width="300" height="400"></iframe>

Does GPT-3 Grasp Metaphors? Identifying Metaphor Mappings with Generative Language Models

Using psycholinguistic and computational experiments we compare the ability of humans and several pre-trained masked language models to correctly identify control dependencies in Spanish sentences such as Jos le prometi/orden a Mara ser ordenado/a' (Joseph promised/ordered Mary to be tidy'). These structures underlie complex anaphoric and agreement relations at the interface of syntax and semantics, allowing us to study lexically-guided antecedent retrieval processes. Our results show that while humans correctly identify the (un)acceptability of the strings, language models often fail to identify the correct antecedent in non-adjacent dependencies, showing their reliance on linearity. Additional experiments on Galician reinforce these conclusions. Our findings are equally valuable for the evaluation of language models' ability to capture linguistic generalizations, as well as for psycholinguistic theories of anaphor resolution.

<iframe src="https://app.sli.do/event/jQes3hFZo3TYjGXEHm6w95/embed/polls/7f68fb91-83f2-47d5-a4cd-8d539603186c" width="300" height="400"></iframe>

Dependency resolution at the syntax-semantics interface: psycholinguistic and computational insights on control dependencies

Recent work has demonstrated the positive impact of incorporating linguistic representations as additional context and scaffolds on the in-domain performance of several NLP tasks. We extend this work by exploring the impact of linguistic representations on cross-domain performance in a few-shot transfer setting. An important question is whether linguistic representations enhance generalizability by providing features that function as cross-domain pivots. We focus on the task of relation extraction on three datasets of procedural text in two domains, cooking and materials science. Our approach augments a popular transformer-based architecture by alternately incorporating syntactic and semantic graphs constructed by freely available off-the-shelf tools. We examine their utility for enhancing generalization, and investigate whether earlier findings, e.g. that semantic representations can be more helpful than syntactic ones, extend to relation extraction in multiple domains. We find that while the inclusion of these graphs results in significantly higher performance in few-shot transfer, both types of graph exhibit roughly equivalent utility.

<iframe src="https://app.sli.do/event/pk54NpNnxuhWHUa3vSMXH2/embed/polls/a09141c7-1650-4fe4-abc5-02c38e17c45f" width="300" height="400"></iframe>

Linguistic representations for fewer-shot relation extraction across domains

Rationalization is to employ a generator and a predictor to construct a self-explaining NLP model in which the generator selects a subset of human-intelligible pieces of the input text to the following predictor. However, rationalization suffers from two key challenges, i.e., spurious correlation and degeneration, where the predictor overfits the spurious or meaningless pieces solely selected by the not-yet well-trained generator and in turn deteriorates the generator. Although many studies have been proposed to address the two challenges, they are usually designed separately and do not take both of them into account. In this paper, we propose a simple yet effective method named MGR to simultaneously solve the two problems. The key idea of MGR is to employ multiple generators such that the occurrence stability of real pieces is improved and more meaningful pieces are delivered to the predictor. Empirically, we show that MGR improves the F1 score by up to 20.9% as compared to state-of-the-art methods.

<iframe src="https://app.sli.do/event/pk54NpNnxuhWHUa3vSMXH2/embed/polls/ff4decbf-9b6d-4654-998d-0b797dd8e0b3" width="300" height="400"></iframe>

MGR: Multi-generator Based Rationalization

The ability to convey relevant and faithful information is critical for many tasks in conditional generation and yet remains elusive for neural seq-to-seq models whose outputs often reveal hallucinations and fail to correctly cover important details. In this work, we advocate planning as a useful intermediate representation for rendering conditional generation less opaque and more grounded.  We propose a new conceptualization of text plans as a sequence of question-answer (QA) pairs and enhance existing datasets (e.g., for summarization) with a QA blueprint operating as a proxy for content selection (i.e., what to say) and planning (i.e., in what order). We obtain blueprints automatically by exploiting state-of-the-art question generation technology and convert input-output pairs into input-blueprint-output tuples. We develop Transformer-based models, each varying in how they incorporate the blueprint in the generated output (e.g., as a global plan or iteratively). Evaluation across metrics and datasets demonstrates that blueprint models are more factual than alternatives which do not resort to planning and allow tighter control of the generation output.

Conditional Generation with a Question-Answering Blueprint

Identifying granular and actionable topics from customer questions (CQ) posted on e-commerce websites helps surface the missing information expected by customers on the product detail page (DP), provide insights to brands and sellers on what critical product information that the customers are looking before making a purchase decision and helps enrich the catalog quality to improve the overall customer experience (CX). We propose a weakly supervised Hierarchical Multi-task Classification Framework (HMCF) to identify topics from customer questions at various granularities. Complexity lies in creating a list of granular topics (taxonomy) for 1000s of product categories and building a scalable classification system. To this end, we introduce a clustering based Taxonomy Creation and Data Labeling (TCDL) module for creating taxonomy and labelled data with minimal supervision. Using TCDL module, taxonomy and labelled data creation task reduces to 2 hours as compared to 2 weeks of manual efforts by a subject matter expert. For classification, we propose a two level HMCF that performs multi-class classification to identify coarse level-1 topic and leverages NLI based label-aware approach to identify granular level-2 topic. We showcase that HMCF (based on BERT and NLI) a) achieves absolute improvement of 13% in Top-1 accuracy over single-task non-hierarchical baselines b) learns a generic domain invariant function that can adapt to constantly evolving taxonomy (open label set) without need of re-training. c) reduces model deployment efforts significantly since it needs only one model that caters to 1000s of product categories.

Weakly supervised hierarchical multi-task classification of customer questions

This paper presents a method for building a personalized open-domain dialogue system to address the WWH (WHAT, WHEN, and HOW) problem for natural response generation in a commercial setting, where personalized dialogue responses are heavily interleaved with casual response turns. The proposed approach involves weighted dataset blending, negative persona information augmentation methods, and the design of personalized conversation datasets to address the challenges of WWH in personalized, open-domain dialogue systems. Our work effectively balances dialogue fluency and tendency to ground, while also introducing a response-type label to improve the controllability and explainability of the grounded responses. The combination of these methods leads to more fluent conversations, as evidenced by subjective human evaluations as well as objective evaluations.

What, When, and How to Ground: Designing User Persona-Aware Conversational Agents for Engaging Dialogue

In recent years, the utilization of Artificial Intelligence (AI) in the contact center industry is on the rise. One area where AI can have a significant impact is in the coaching of contact center agents. By analyzing call transcripts, AI can quickly determine which calls are most relevant for coaching purposes, and provide relevant feedback and insights to the contact center manager or supervisor. In this paper, we present "AI Coach Assis", which leverages the pre-trained transformer-based language models to determine whether a given call is coachable or not based on the quality assurance (QA) queries/questions asked by the contact center managers or supervisors. The system was trained and evaluated on a large dataset collected from real-world contact centers and provides an efficient and effective way to determine which calls are most relevant for coaching purposes. Extensive experimental evaluation demonstrates the potential of AI Coach Assist to improve the coaching process, resulting in enhancing the performance of contact center agents.

AI Coach Assist: An Automated Approach for Call Recommendation in Contact Centers for Agent Coaching

Recently, the recognition of flat, nested, and discontinuous entities by a unified generative model framework has received increasing attention both in the research field and industry. However, the current generative NER methods force the entities to be generated in a predefined order, suffering from error propagation and inefficient decoding. In this work, we propose a unified non-autoregressive generation (NAG) framework for general NER tasks, referred to as NAG-NER. First, we propose to generate entities as a set instead of a sequence, avoiding error propagation. Second, we propose incorporating NAG in NER tasks for efficient decoding by treating each entity as a target sequence. Third, to enhance the generation performances of the NAG decoder, we employ the NAG encoder to detect potential entity mentions. Extensive experiments show that our NAG-NER model outperforms the state-of-the-art generative NER models on three benchmark NER datasets of different types and two of our proprietary NER tasks.\footnote{Code will be publicly available to the research community upon acceptance.}

NAG-NER: a Unified Non-Autoregressive Generation Framework for Various NER Tasks

Misspelled search queries in e-commerce can lead to empty or irrelevant products. Besides inadvertent typing mistakes, most spell mistakes occur because the user does not know the correct spelling, hence typing it as it is pronounced colloquially. This colloquial typing creates countless misspelling patterns for a single correct query. In this paper, we first systematically analyze and group different spell errors into error classes and then leverage the state-of-the-art Transformer model for contextual spell correction. We overcome the constraint of limited human labelled data by proposing novel synthetic data generation techniques for voluminous generation of training pairs needed by data hungry Transformers, without any human intervention. We further utilize weakly supervised data coupled with curriculum learning strategies to improve on tough spell mistakes without regressing on the easier ones. We show significant improvements from our model on human labeled data and online A/B experiments against multiple state-of-art models.

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