pwc-archive/papers-with-abstracts huggingface dataset:
DoSSIER at MedVidQA 2022: Text-based Approaches to Medical Video Answer Localization Problem
Authors: Wojciech Kusa, Georgios Peikos, Óscar Espitia, Allan Hanbury, Gabriella Pasi
Date: 2022-05-01 00:00:00
Proceeding: BioNLP (ACL) 2022 5

This paper describes our contribution to the Answer Localization track of the MedVidQA 2022 Shared Task. We propose two answer localization approaches that use only textual information extracted from the video. In particular, our approaches exploit the text extracted from the video’s transcripts along with the text displayed in the video’s frames to create a set of features. Having created a set of features that represents a video’s textual information, we employ four different models to measure the similarity between a video’s segment and a corresponding question. Then, we employ two different methods to obtain the start and end times of the identified answer. One of them is based on a random forest regressor, whereas the other one uses an unsupervised peak detection model to detect the answer’s start time. Our findings suggest that for this task, leveraging only text-related features (transmitted either verbally or visually) and using a small amount of training data, lead to significant improvements over the benchmark Video Span Localization model that is based on deep neural networks.
MSAMSum: Towards Benchmarking Multi-lingual Dialogue Summarization
Authors: Xiachong Feng, Xiaocheng Feng, Bing Qin
Date: 2022-05-01 00:00:00
Proceeding: dialdoc (ACL) 2022 5
Tasks: Benchmarking, dialogue summary, Translation

Dialogue summarization helps users capture salient information from various types of dialogues has received much attention recently. However, current works mainly focus on English dialogue summarization, leaving other languages less well explored. Therefore, we present a multi-lingual dialogue summarization dataset, namely MSAMSum, which covers dialogue-summary pairs in six languages. Specifically, we derive MSAMSum from the standard SAMSum using sophisticated translation techniques and further employ two methods to ensure the integral translation quality and summary factual consistency. Given the proposed MSAMum, we systematically set up five multi-lingual settings for this task, including a novel mix-lingual dialogue summarization setting. To illustrate the utility of our dataset, we benchmark various experiments with pre-trained models under different settings and report results in both supervised and zero-shot manners. We also discuss some future works towards this task to motivate future researches.
Pseudo Ambiguous and Clarifying Questions Based on Sentence Structures Toward Clarifying Question Answering System
Authors: Yuya Nakano, Seiya Kawano, Koichiro Yoshino, Katsuhito Sudoh, Satoshi Nakamura
Date: 2022-05-01 00:00:00
Proceeding: dialdoc (ACL) 2022 5
Tasks: Question Answering, Question Generation, Question-Generation, Sentence

Question answering (QA) with disambiguation questions is essential for practical QA systems because user questions often do not contain information enough to find their answers. We call this task clarifying question answering, a task to find answers to ambiguous user questions by disambiguating their intents through interactions. There are two major problems in building a clarifying question answering system: data preparation of possible ambiguous questions and the generation of clarifying questions. In this paper, we tackle these problems by sentence generation methods using sentence structures. Ambiguous questions are generated by eliminating a part of a sentence considering the sentence structure. Clarifying the question generation method based on case frame dictionary and sentence structure is also proposed. Our experimental results verify that our pseudo ambiguous question generation successfully adds ambiguity to questions. Moreover, the proposed clarifying question generation recovers the performance drop by asking the user for missing information.
Conversation- and Tree-Structure Losses for Dialogue Disentanglement
Authors: Tianda Li, Jia-Chen Gu, Zhen-Hua Ling, Quan Liu
Date: 2022-05-01 00:00:00
Proceeding: dialdoc (ACL) 2022 5
Tasks: Disentanglement

When multiple conversations occur simultaneously, a listener must decide which conversation each utterance is part of in order to interpret and respond to it appropriately. This task is referred as dialogue disentanglement. A significant drawback of previous studies on disentanglement lies in that they only focus on pair-wise relationships between utterances while neglecting the conversation structure which is important for conversation structure modeling. In this paper, we propose a hierarchical model, named Dialogue BERT (DIALBERT), which integrates the local and global semantics in the context range by using BERT to encode each message-pair and using BiLSTM to aggregate the chronological context information into the output of BERT. In order to integrate the conversation structure information into the model, two types of loss of conversation-structure loss and tree-structure loss are designed. In this way, our model can implicitly learn and leverage the conversation structures without being restricted to the lack of explicit access to such structures during the inference stage. Experimental results on two large datasets show that our method outperforms previous methods by substantial margins, achieving great performance on dialogue disentanglement.
Conversational Search with Mixed-Initiative - Asking Good Clarification Questions backed-up by Passage Retrieval
Authors: Yosi Mass, Doron Cohen, Asaf Yehudai, David Konopnicki
Date: 2022-05-01 00:00:00
Proceeding: dialdoc (ACL) 2022 5
Tasks: Conversational Search, Passage Retrieval, Retrieval

We deal with the scenario of conversational search, where user queries are under-specified or ambiguous. This calls for a mixed-initiative setup. User-asks (queries) and system-answers, as well as system-asks (clarification questions) and user response, in order to clarify her information needs. We focus on the task of selecting the next clarification question, given conversation context. Our method leverages passage retrieval from background content to fine-tune two deep-learning models for ranking candidate clarification questions. We evaluated our method on two different use-cases. The first is an open domain conversational search in a large web collection. The second is a task-oriented customer-support setup.We show that our method performs well on both use-cases.
Graph-combined Coreference Resolution Methods on Conversational Machine Reading Comprehension with Pre-trained Language Model
Authors: Zhaodong Wang, Kazunori Komatani
Date: 2022-05-01 00:00:00
Proceeding: dialdoc (ACL) 2022 5
Tasks: coreference-resolution, Coreference Resolution, Graph Neural Network, Language Modeling, Language Modelling, Machine Reading Comprehension, Reading Comprehension

Coreference resolution such as for anaphora has been an essential challenge that is commonly found in conversational machine reading comprehension (CMRC). This task aims to determine the referential entity to which a pronoun refers on the basis of contextual information. Existing approaches based on pre-trained language models (PLMs) mainly rely on an end-to-end method, which still has limitations in clarifying referential dependency. In this study, a novel graph-based approach is proposed to integrate the coreference of given text into graph structures (called coreference graphs), which can pinpoint a pronoun’s referential entity. We propose two graph-combined methods, evidence-enhanced and the fusion model, for CMRC to integrate coreference graphs from different levels of the PLM architecture. Evidence-enhanced refers to textual level methods that include an evidence generator (for generating new text to elaborate a pronoun) and enhanced question (for rewriting a pronoun in a question) as PLM input. The fusion model is a structural level method that combines the PLM with a graph neural network. We evaluated these approaches on a CoQA pronoun-containing dataset and the whole CoQA dataset. The result showed that our methods can outperform baseline PLM methods with BERT and RoBERTa.
Construction of Hierarchical Structured Knowledge-based Recommendation Dialogue Dataset and Dialogue System
Authors: Takashi Kodama, Ribeka Tanaka, Sadao Kurohashi
Date: 2022-05-01 00:00:00
Proceeding: dialdoc (ACL) 2022 5
Tasks: Movie Recommendation

We work on a recommendation dialogue system to help a user understand the appealing points of some target (e.g., a movie). In such dialogues, the recommendation system needs to utilize structured external knowledge to make informative and detailed recommendations. However, there is no dialogue dataset with structured external knowledge designed to make detailed recommendations for the target. Therefore, we construct a dialogue dataset, Japanese Movie Recommendation Dialogue (JMRD), in which the recommender recommends one movie in a long dialogue (23 turns on average). The external knowledge used in this dataset is hierarchically structured, including title, casts, reviews, and plots. Every recommender’s utterance is associated with the external knowledge related to the utterance. We then create a movie recommendation dialogue system that considers the structure of the external knowledge and the history of the knowledge used. Experimental results show that the proposed model is superior in knowledge selection to the baseline models.
G4: Grounding-guided Goal-oriented Dialogues Generation with Multiple Documents
Authors: Shiwei Zhang, Yiyang Du, Guanzhong Liu, Zhao Yan, Yunbo Cao
Date: 2022-05-01 00:00:00
Proceeding: dialdoc (ACL) 2022 5
Tasks: Machine Reading Comprehension, Reading Comprehension, Response Generation

Goal-oriented dialogues generation grounded in multiple documents(MultiDoc2Dial) is a challenging and realistic task. Unlike previous works which treat document-grounded dialogue modeling as a machine reading comprehension task from single document, MultiDoc2Dial task faces challenges of both seeking information from multiple documents and generating conversation response simultaneously. This paper summarizes our entries to agent response generation subtask in MultiDoc2Dial dataset. We propose a three-stage solution, Grounding-guided goal-oriented dialogues generation(G4), which predicts groundings from retrieved passages to guide the generation of the final response. Our experiments show that G4 achieves SacreBLEU score of 31.24 and F1 score of 44.6 which is 60.7% higher than the baseline model.
UGent-T2K at the 2nd DialDoc Shared Task: A Retrieval-Focused Dialog System Grounded in Multiple Documents
Authors: Yiwei Jiang, Amir Hadifar, Johannes Deleu, Thomas Demeester, Chris Develder
Date: 2022-05-01 00:00:00
Proceeding: dialdoc (ACL) 2022 5
Tasks: Decoder, Passage Retrieval, Response Generation, Retrieval

This work presents the contribution from the Text-to-Knowledge team of Ghent University (UGent-T2K) to the MultiDoc2Dial shared task on modeling dialogs grounded in multiple documents. We propose a pipeline system, comprising (1) document retrieval, (2) passage retrieval, and (3) response generation. We engineered these individual components mainly by, for (1)-(2), combining multiple ranking models and adding a final LambdaMART reranker, and, for (3), by adopting a Fusion-in-Decoder (FiD) model. We thus significantly boost the baseline system’s performance (over +10 points for both F1 and SacreBLEU). Further, error analysis reveals two major failure cases, to be addressed in future work: (i) in case of topic shift within the dialog, retrieval often fails to select the correct grounding document(s), and (ii) generation sometimes fails to use the correctly retrieved grounding passage. Our code is released at this link.
Grounded Dialogue Generation with Cross-encoding Re-ranker, Grounding Span Prediction, and Passage Dropout
Authors: Kun Li, Tianhua Zhang, Liping Tang, Junan Li, Hongyuan Lu, Xixin Wu, Helen Meng
Date: 2022-05-01 00:00:00
Proceeding: dialdoc (ACL) 2022 5
Tasks: Dialogue Generation, Response Generation

MultiDoc2Dial presents an important challenge on modeling dialogues grounded with multiple documents. This paper proposes a pipeline system of “retrieve, re-rank, and generate”, where each component is individually optimized. This enables the passage re-ranker and response generator to fully exploit training with ground-truth data. Furthermore, we use a deep cross-encoder trained with localized hard negative passages from the retriever. For the response generator, we use grounding span prediction as an auxiliary task to be jointly trained with the main task of response generation. We also adopt a passage dropout and regularization technique to improve response generation performance. Experimental results indicate that the system clearly surpasses the competitive baseline and our team CPII-NLP ranked 1st among the public submissions on ALL four leaderboards based on the sum of F1, SacreBLEU, METEOR and RougeL scores.
A Knowledge storage and semantic space alignment Method for Multi-documents dialogue generation
Authors: Minjun Zhu, Bin Li, Yixuan Weng, Fei Xia
Date: 2022-05-01 00:00:00
Proceeding: dialdoc (ACL) 2022 5
Tasks: Articles, Dialogue Generation, Language Modeling, Language Modelling, Question Answering, Response Generation, Retrieval

Question Answering (QA) is a Natural Language Processing (NLP) task that can measure language and semantics understanding ability, it requires a system not only to retrieve relevant documents from a large number of articles but also to answer corresponding questions according to documents. However, various language styles and sources of human questions and evidence documents form the different embedding semantic spaces, which may bring some errors to the downstream QA task. To alleviate these problems, we propose a framework for enhancing downstream evidence retrieval by generating evidence, aiming at improving the performance of response generation. Specifically, we take the pre-training language model as a knowledge base, storing documents’ information and knowledge into model parameters. With the Child-Tuning approach being designed, the knowledge storage and evidence generation avoid catastrophic forgetting for response generation. Extensive experiments carried out on the multi-documents dataset show that the proposed method can improve the final performance, which demonstrates the effectiveness of the proposed framework.
Improving Multiple Documents Grounded Goal-Oriented Dialog Systems via Diverse Knowledge Enhanced Pretrained Language Model
Authors: Yunah Jang, Dongryeol Lee, Hyung Joo Park, Taegwan Kang, Hwanhee Lee, Hyunkyung Bae, Kyomin Jung
Date: 2022-05-01 00:00:00
Proceeding: dialdoc (ACL) 2022 5
Tasks: Goal-Oriented Dialog, Language Modeling, Language Modelling, Multi-Task Learning, Passage Retrieval, Response Generation, Retrieval, Retrieval-augmented Generation

In this paper, we mainly discuss about our submission to MultiDoc2Dial task, which aims to model the goal-oriented dialogues grounded in multiple documents. The proposed task is split into grounding span prediction and agent response generation. The baseline for the task is the retrieval augmented generation model, which consists of a dense passage retrieval model for the retrieval part and the BART model for the generation part. The main challenge of this task is that the system requires a great amount of pre-trained knowledge to generate answers grounded in multiple documents. To overcome this challenge, we adopt model pretraining, fine-tuning, and multi-task learning to enhance our model’s coverage of pretrained knowledge. We experimented with various settings of our method to show the effectiveness of our approaches.
Docalog: Multi-document Dialogue System using Transformer-based Span Retrieval
Authors: Sayed Hesam Alavian, Ali Satvaty, Sadra Sabouri, Ehsaneddin Asgari, Hossein Sameti
Date: 2022-05-01 00:00:00
Proceeding: dialdoc (ACL) 2022 5
Tasks: Response Generation, Retrieval

Information-seeking dialogue systems, including knowledge identification and response generation, aim to respond to users with fluent, coherent, and informative answers based on users’ needs. This paper discusses our proposed approach, Docalog, for the DialDoc-22 (MultiDoc2Dial) shared task. Docalog identifies the most relevant knowledge in the associated document, in a multi-document setting. Docalog, is a three-stage pipeline consisting of (1) a document retriever model (DR. TEIT), (2) an answer span prediction model, and (3) an ultimate span picker deciding on the most likely answer span, out of all predicted spans. In the test phase of MultiDoc2Dial 2022, Docalog achieved f1-scores of 36.07% and 28.44% and SacreBLEU scores of 23.70% and 20.52%, respectively on the MDD-SEEN and MDD-UNSEEN folds.
R3 : Refined Retriever-Reader pipeline for Multidoc2dial
Authors: Srijan Bansal, Suraj Tripathi, Sumit Agarwal, Sireesh Gururaja, Aditya Srikanth Veerubhotla, Ritam Dutt, Teruko Mitamura, Eric Nyberg
Date: 2022-05-01 00:00:00
Proceeding: dialdoc (ACL) 2022 5
Tasks: Conversational Question Answering, Decoder, Passage Retrieval, Question Answering, RAG, Retrieval

In this paper, we present our submission to the DialDoc shared task based on the MultiDoc2Dial dataset. MultiDoc2Dial is a conversational question answering dataset that grounds dialogues in multiple documents. The task involves grounding a user’s query in a document followed by generating an appropriate response. We propose several improvements over the baseline’s retriever-reader architecture to aid in modeling goal-oriented dialogues grounded in multiple documents. Our proposed approach employs sparse representations for passage retrieval, a passage re-ranker, the fusion-in-decoder architecture for generation, and a curriculum learning training paradigm. Our approach shows a 12 point improvement in BLEU score compared to the baseline RAG model.
DialDoc 2022 Shared Task: Open-Book Document-grounded Dialogue Modeling
Authors: Song Feng, Siva Patel, Hui Wan
Date: 2022-05-01 00:00:00
Proceeding: dialdoc (ACL) 2022 5
Tasks: Conversational Question Answering, Question Answering

The paper presents the results of the Shared Task hosted by the Second DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering co-located at ACL 2022. The primary goal of this Shared Task is to build goal-oriented information-seeking conversation systems that are grounded in the domain documents, where each dialogue could correspond to multiple subtasks that are based on different documents. The task is to generate agent responses in natural language given the dialogue and document contexts. There are two task settings and leaderboards based on (1) the same sets of domains (SEEN) and (2) one unseen domain (UNSEEN). There are over 20 teams participating in Dev Phase and 8 teams participating in both Dev and Test Phases. Multiple submissions significantly outperform the baseline. The best-performing system achieves 52.06 F1 and the total of 191.30 on the SEEN task; and 34.65 F1 and the total of 130.79 on the UNSEEN task.
Handling Comments in Documents through Interactions
Authors: Elnaz Nouri, Carlos Toxtli
Date: 2022-05-01 00:00:00
Proceeding: dialdoc (ACL) 2022 5
Tasks: Management

Comments are widely used by users in collaborative documents every day. The documents’ comments enable collaborative editing and review dynamics, transforming each document into a context-sensitive communication channel. Understanding the role of comments in communication dynamics within documents is the first step towards automating their management. In this paper we propose the first ever taxonomy for different types of in-document comments based on analysis of a large scale dataset of public documents from the web. We envision that the next generation of intelligent collaborative document experiences allow interactive creation and consumption of content, there We also introduce the components necessary for developing novel tools that automate the handling of comments through natural language interaction with the documents. We identify the commands that users would use to respond to various types of comments. We train machine learning algorithms to recognize the different types of comments and assess their feasibility. We conclude by discussing some of the implications for the design of automatic document management tools.
Task2Dial: A Novel Task and Dataset for Commonsense-enhanced Task-based Dialogue Grounded in Documents
Authors: Carl Strathearn, Dimitra Gkatzia
Date: 2022-05-01 00:00:00
Proceeding: dialdoc (ACL) 2022 5

This paper proposes a novel task on commonsense-enhanced task-based dialogue grounded in documents and describes the Task2Dial dataset, a novel dataset of document-grounded task-based dialogues, where an Information Giver (IG) provides instructions (by consulting a document) to an Information Follower (IF), so that the latter can successfully complete the task. In this unique setting, the IF can ask clarification questions which may not be grounded in the underlying document and require commonsense knowledge to be answered. The Task2Dial dataset poses new challenges: (1) its human reference texts show more lexical richness and variation than other document-grounded dialogue datasets; (2) generating from this set requires paraphrasing as instructional responses might have been modified from the underlying document; (3) requires commonsense knowledge, since questions might not necessarily be grounded in the document; (4) generating requires planning based on context, as task steps need to be provided in order. The Task2Dial dataset contains dialogues with an average 18.15 number of turns and 19.79 tokens per turn, as compared to 12.94 and 12 respectively in existing datasets. As such, learning from this dataset promises more natural, varied and less template-like system utterances.
Data-to-text systems as writing environment
Authors: Adela Schneider, Andreas Madsack, Johanna Heininger, Ching-Yi Chen, Robert Weißgraeber
Date: 2022-05-01 00:00:00
Proceeding: In2Writing (ACL) 2022 5

Today, data-to-text systems are used as commercial solutions for automated text productionof large quantities of text. Therefore, they already represent a new technology of writing.This new technology requires the author, asan act of writing, both to configure a systemthat then takes over the transformation into areal text, but also to maintain strategies of traditional writing. What should an environmentlook like, where a human guides a machineto write texts? Based on a comparison of theNLG pipeline architecture with the results ofthe research on the human writing process, thispaper attempts to take an overview of whichtasks need to be solved and which strategiesare necessary to produce good texts in this environment. From this synopsis, principles for thedesign of data-to-text systems as a functioningwriting environment are then derived.
A Design Space for Writing Support Tools Using a Cognitive Process Model of Writing
Authors: Katy Gero, Alex Calderwood, Charlotte Li, Lydia Chilton
Date: 2022-05-01 00:00:00
Proceeding: In2Writing (ACL) 2022 5

Improvements in language technology have led to an increasing interest in writing support tools. In this paper we propose a design space for such tools based on a cognitive process model of writing. We conduct a systematic review of recent computer science papers that present and/or study such tools, analyzing 30 papers from the last five years using the design space. Tools are plotted according to three distinct cognitive processes–planning, translating, and reviewing–and the level of constraint each process entails. Analyzing recent work with the design space shows that highly constrained planning and reviewing are under-studied areas that recent technology improvements may now be able to serve. Finally, we propose shared evaluation methodologies and tasks that may help the field mature.