Croatia

A novel feature represents a cluster of semantically equivalent novel user requests e.g., requests to play a song on a service or read user&#39;s messages.
Detecting and supporting novel features is crucial towards wider adoption of dialog systems by end users.
Intuitively, features are represented by a combination of intents, slot types and/or their values. For example, while playing a song is a feature represented by a single intent (PlayMusic) only, playing a song on a service is another feature represented by the combination of PlayMusic intent and ServiceName slot type.
Prior work on novelty detection limits the scope of features to those represented by novel single intents, leading to (1) giant clusters spanning several user-perceived fine-grained features belonging to the same intent, (2) incoherent interpretation of clusters from users&#39; perspective (no direct connection to some user-perceived feature), and (3) missing those features spanning several intents.
In this work, we introduce feature discovery as opposed to single intent discovery, which aims at discovering novel features spanning a combination of intents and slots, and present a technique for discovering novel features from user utterances. Experiments on two datasets demonstrate the effectiveness of our approach and consistently show its ability to detect novel features.

EACL 2023

Novel Feature Discovery for Task-Oriented Dialog Systems

feature discovery

clustering

classification

## 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

A novel feature represents a cluster of semantically equivalent novel user requests e.g., requests to play a song on a service or read user's messages.
Detecting and supporting novel features is crucial towards wider adoption of dialog systems by end users.
Intuitively, features are represented by a combination of intents, slot types and/or their values. For example, while playing a song is a feature represented by a single intent (PlayMusic) only, playing a song on a service is another feature represented by the combination of PlayMusic intent and ServiceName slot type.
Prior work on novelty detection limits the scope of features to those represented by novel single intents, leading to (1) giant clusters spanning several user-perceived fine-grained features belonging to the same intent, (2) incoherent interpretation of clusters from users' perspective (no direct connection to some user-perceived feature), and (3) missing those features spanning several intents.
In this work, we introduce feature discovery as opposed to single intent discovery, which aims at discovering novel features spanning a combination of intents and slots, and present a technique for discovering novel features from user utterances. Experiments on two datasets demonstrate the effectiveness of our approach and consistently show its ability to detect novel features.

findings / work in progress

### Welcome to the 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL) 
Continuing its mission of expanding and involving the science community of all European countries, EACL had selected the Ukrainian community for the 16th EACL, which took place online due to the COVID pandemic. Unfortunately, the ongoing war made the organisation in Kyiv impossible. Considering the importance of physical interaction among researchers, especially after the restrictions imposed by the COVID pandemic, in addition to an online mode, the conference will be held in Dubrovnik, Croatia, from 2 to 6 of May, 2023. The original aim of strengthening the connection with the Ukrainian community will not change as our program will feature a dedicated session and a workshop to highlight work on Ukrainian language technologies. As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

You need to register for the conference in order to access this site. Please visit https://2023.eacl.org/registration for more information.

As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

Closed-book question answering (QA) requires a model to directly answer an open-domain question without access to any external knowledge. Prior work on closed-book QA either directly finetunes or prompts a pretrained language model (LM) to leverage the stored knowledge. However, they do not fully exploit the parameterized knowledge. To address this inefficiency, we propose a two-stage, closed-book QA framework which employs a coarse-to-fine approach to extract the relevant knowledge and answer a question. We first generate a related context for a given question by prompting a pretrained LM. We then prompt the same LM to generate an answer using the generated context and the question. Additionally, we marginalize over the generated contexts to improve the accuracies and reduce context uncertainty. Experimental results on three QA benchmarks show that our method significantly outperforms previous closed-book QA methods. For example on TriviaQA, our method improves exact match accuracy from 55.3% to 68.6%, and is on par with open-book QA methods (68.6% vs. 68.0%). Our results show that our new methodology is able to better exploit the stored knowledge in pretrained LMs without adding extra learnable parameters or needing finetuning, and paves the way for hybrid models that integrate pretrained LMs with external knowledge.

Context Generation Improves Open Domain Question Answering

We present Reddit Health Online Talk (RedHOT), a corpus of 22,000 richly annotated social media posts from Reddit spanning 24 health conditions. 
Annotations include demarcations of spans corresponding to medical claims, personal experiences, and questions.
We collect additional granular annotations on identified claims.
Specifically, we mark snippets that describe patient Populations, Interventions, and Outcomes (PIO elements) within these. 
Using this corpus, we introduce the task of retrieving trustworthy evidence relevant to a given claim made on social media. 
We propose a new method to automatically derive (noisy) supervision for this task which we use to train a dense retrieval model; this outperforms baseline models. 
Manual evaluation 
of retrieval results performed by medical doctors indicate that while our system performance is promising, there is considerable room for improvement.
We release all annotations collected (and scripts to assemble the dataset), and all code necessary to reproduce the results in this paper at: https://sominw.com/redhot.

RedHOT: A Corpus of Annotated Medical Questions, Experiences, and Claims on Social Media

Existing language and vision models achieve impressive performance in image-text understanding. Yet, it is an open question to what extent they can be used for language understanding in 3D environments and whether they implicitly acquire 3D object knowledge, e.g. about different views of an object.
In this paper, we investigate whether a state-of-the-art language and vision model, CLIP, is able to ground perspective descriptions of a 3D object and identify canonical views of common objects based on text queries.
We present an evaluation framework that uses a circling camera around a 3D object to generate images from different viewpoints and evaluate them in terms of their similarity to natural language descriptions.
We find that a pre-trained CLIP model performs poorly on most canonical views and that fine-tuning using hard negative sampling and random contrasting yields good results even under conditions with little available training data.

Paparazzi: A Deep Dive into the Capabilities of Language and Vision Models for Grounding Viewpoint Descriptions

Collecting high quality conversational data can be very expensive for most applications and infeasible for others due to privacy, ethical, or similar concerns. A promising direction to tackle this problem is to generate synthetic dialogues by prompting large language models. In this work, we use a small set of expert-written conversations as in-context examples to synthesize a social conversation dataset using prompting. We perform several thorough evaluations of our synthetic conversations compared to human-collected conversations. This includes various dimensions of conversation quality with human evaluation directly on the synthesized conversations, and interactive human evaluation of chatbots fine-tuned on the synthetically generated dataset. We additionally demonstrate that this prompting approach is generalizable to multi-party conversations, providing potential to create new synthetic data for multi-party tasks. Our synthetic multi-party conversations were rated more favorably across all measured dimensions compared to conversation excerpts sampled from a human-collected multi-party dataset.

PLACES: Prompting Language Models for Social Conversation Synthesis

Federated learning is a training paradigm that learns from multiple distributed users without aggregating data on a centralized server, promising the ability to deploy machine-learning to a diverse population of users without first collecting large, labeled datasets.
As federated learning involves averaging gradient updates across a decentralized population, there is a growing need for personalization of federated learning systems (i.e. conversational agents must personalize to individual users and the context of an interaction).
In this work, we propose a new direction for personalization research within federated learning, leveraging both personal embeddings and shared context embeddings.
We also present an approach to predict these "preference" embeddings, enabling personalization without backpropagation. 
Compared to state-of-the-art personalization baselines, our approach achieves a 50% improvement in test-time perplexity using 0.001% of the memory required by baseline approaches, and achieving greater sample- and compute-efficiency.

FedPerC: Federated Learning for Language Generation with Personal and Context Preference Embeddings

We consider the problem of segmenting unformatted text and transcripts linearly based on their topical structure. While prior approaches explicitly train to predict segment boundaries, our proposed approach solves this task by inferring the hierarchical segmentation structure associated with the input text fragment. Given the lack of a large annotated dataset for this task, we propose a data curation strategy and create a corpus of over 700K Wikipedia articles with their hierarchical structures. We then propose the first supervised approach to generating hierarchical segmentation structures based on these annotations. Our method, in particular, is based on a neural conditional random field (CRF), which explicitly models the statistical dependency between a node and its constituent child nodes. We introduce a new data augmentation scheme as part of our model training strategy, which involves sampling a variety of node aggregations, permutations, and removals, all of which help capture fine-grained and coarse topical shifts in the data and improve model performance. Extensive experiments show that our model outperforms or achieves competitive performance when compared to previous state-of-the-art algorithms in the following settings: rich-resource, cross-domain transferability, few-shot supervision, and segmentation when topic label annotations are provided.

A Neural CRF-based Hierarchical Approach for Linear Text Segmentation

Financial information is generated and distributed across the world, resulting in a vast amount of domain-specific multilingual data. Multilingual models adapted to the financial domain would ease deployment when an organization needs to work with multiple languages on a regular basis. For the development and evaluation of such models, there is a need for multilingual financial language processing datasets. We describe MultiFin -- a publicly available financial dataset consisting of real-world article headlines covering 15 languages across different writing systems and language families. The dataset consists of hierarchical label structure providing two classification tasks: multi-label and multi-class. We develop our annotation schema based on a real-world application and annotate our dataset using both 'label by native-speaker' and 'translate-then-label' approaches. The evaluation of several popular multilingual models, e.g., mBERT, XLM-R, and mT5, show that although decent accuracy can be achieved in high-resource languages, there is substantial room for improvement in low-resource languages.

MultiFin: A Dataset for Multilingual Financial NLP

In recent years, the pattern of news consumption has been changing. The most popular multimedia news formats are now multimodal - the reader is often presented not only with a textual article but also with a short, vivid video. To draw the attention of the reader, such video-based articles are usually presented as a short textual summary paired with an image thumbnail.
In this paper, we introduce MLASK (MultimodaL Article Summarization Kit) - a new dataset of video-based news articles paired with a textual summary and a cover picture, all obtained by automatically crawling several news websites. We demonstrate how the proposed dataset can be used to model the task of multimodal summarization by training a Transformer-based neural model. We also examine the effects of pre-training when the usage of generative pre-trained language models helps to improve the model performance, but (additional) pre-training on the simpler task of text summarization yields even better results. Our experiments suggest that the benefits of pre-training and using additional modalities in the input are not orthogonal.

MLASK: Multimodal Summarization of Video-based News Articles

Novel intent discovery automates the process of grouping similar messages (questions) to identify previously unknown intents. However, current research focuses on publicly available datasets which have only the question field and significantly differ from real-life datasets. This paper proposes methods to improve the intent discovery pipeline deployed in a large e-commerce platform. We show the benefit of pre-training language models on in-domain data: both self-supervised and with weak supervision. We also devise the best method to utilize the conversational structure (i.e., question and answer) of real-life datasets during fine-tuning for clustering tasks, which we call Conv. All our methods combined to fully utilize real-life datasets give up to 33pp performance boost over state-of-the-art Constrained Deep Adaptive Clustering (CDAC) model for question only. By comparison CDAC model for the question data only gives only up to 13pp performance boost over the naive baseline.

Going beyond research datasets: Novel intent discovery in the industry setting

The rapid development of large pretrained language models has revolutionized not only the field of Natural Language Generation (NLG) but also its evaluation. Inspired by the recent work of BARTScore: a metric leveraging the BART language model to evaluate the quality of generated text from various aspects, we introduce DATScore. DATScore uses data augmentation techniques to improve the evaluation of machine translation. Our main finding is that introducing data augmented translations of the source and reference texts is greatly helpful in evaluating the quality of the generated translation. We also propose two novel score averaging and term weighting strategies to improve the original score computing process of BARTScore. Experimental results on WMT show that DATScore correlates better with human meta-evaluations than the other recent state-of-the-art metrics, especially for low-resource languages. Ablation studies demonstrate the value added by our new scoring strategies. Moreover, we report in our extended experiments the performance of DATScore on 3 NLG tasks other than translation.

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Context Generation Improves Open Domain Question Answering

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