pwc-archive/papers-with-abstracts huggingface dataset:
DialogueEIN: Emotion Interaction Network for Dialogue Affective Analysis
Authors: Yuchen Liu, Jinming Zhao, Jingwen Hu, Ruichen Li, Qin Jin
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Emotion Recognition, Emotion Recognition in Conversation

Emotion Recognition in Conversation (ERC) has attracted increasing attention in the affective computing research field. Previous works have mainly focused on modeling the semantic interactions in the dialogue and implicitly inferring the evolution of the speakers’ emotional states. Few works have considered the emotional interactions, which directly reflect the emotional evolution of speakers in the dialogue. According to psychological and behavioral studies, the emotional inertia and emotional stimulus are important factors that affect the speaker’s emotional state in conversations. In this work, we propose a novel Dialogue Emotion Interaction Network, DialogueEIN, to explicitly model the intra-speaker, inter-speaker, global and local emotional interactions to respectively simulate the emotional inertia, emotional stimulus, global and local emotional evolution in dialogues. Extensive experiments on four ERC benchmark datasets, IEMOCAP, MELD, EmoryNLP and DailyDialog, show that our proposed DialogueEIN considering emotional interaction factors can achieve superior or competitive performance compared to state-of-the-art methods. Our codes and models are released.
Speaker Clustering in Textual Dialogue with Pairwise Utterance Relation and Cross-corpus Dialogue Act Supervision
Authors: Zhihua Su, Qiang Zhou
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Clustering, Cross-corpus, Dialogue Act Classification, Language Modeling, Language Modelling, Relation, Semantic Similarity, Semantic Textual Similarity

We propose a speaker clustering model for textual dialogues, which groups the utterances of a multi-party dialogue without speaker annotations, so that the actual speakers are identical inside each cluster. We find that, without knowing the speakers, the interactions between utterances are still implied in the text, which suggest the relations between speakers. In this work, we model the semantic content of utterance with a pre-trained language model, and the relations between speakers with an utterance-level pairwise matrix. The semantic content representation can be further instructed by cross-corpus dialogue act modeling. The speaker labels are finally generated by spectral clustering. Experiments show that our model outperforms the sequence classification baseline, and benefits from the auxiliary dialogue act classification task. We also discuss the detail of determining the number of speakers (clusters), eliminating the interference caused by semantic similarity, and the impact of utterance distance.
TopKG: Target-oriented Dialog via Global Planning on Knowledge Graph
Authors: Zhitong Yang, Bo wang, Jinfeng Zhou, Yue Tan, Dongming Zhao, Kun Huang, Ruifang He, Yuexian Hou
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Response Generation

Target-oriented dialog aims to reach a global target through multi-turn conversation. The key to the task is the global planning towards the target, which flexibly guides the dialog concerning the context. However, existing target-oriented dialog works take a local and greedy strategy for response generation, where global planning is absent. In this work, we propose global planning for target-oriented dialog on a commonsense knowledge graph (KG). We design a global reinforcement learning with the planned paths to flexibly adjust the local response generation model towards the global target. We also propose a KG-based method to collect target-oriented samples automatically from the chit-chat corpus for model training. Experiments show that our method can reach the target with a higher success rate, fewer turns, and more coherent responses.
Extractive Summarisation for German-language Data: A Text-level Approach with Discourse Features
Authors: Freya Hewett, Manfred Stede
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Sentence

We examine the link between facets of Rhetorical Structure Theory (RST) and the selection of content for extractive summarisation, for German-language texts. For this purpose, we produce a set of extractive summaries for a dataset of German-language newspaper commentaries, a corpus which already has several layers of annotation. We provide an in-depth analysis of the connection between summary sentences and several RST-based features and transfer these insights to various automated summarisation models. Our results show that RST features are informative for the task of extractive summarisation, particularly nuclearity and relations at sentence-level.
End-to-End Neural Bridging Resolution
Authors: Hideo Kobayashi, Yufang Hou, Vincent Ng
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: coreference-resolution, Coreference Resolution

The state of bridging resolution research is rather unsatisfactory: not only are state-of-the-art resolvers evaluated in unrealistic settings, but the neural models underlying these resolvers are weaker than those used for entity coreference resolution. In light of these problems, we evaluate bridging resolvers in an end-to-end setting, strengthen them with better encoders, and attempt to gain a better understanding of them via perturbation experiments and a manual analysis of their outputs.
Investigating the Performance of Transformer-Based NLI Models on Presuppositional Inferences
Authors: Jad Kabbara, Jackie Chi Kit Cheung
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10

Presuppositions are assumptions that are taken for granted by an utterance, and identifying them is key to a pragmatic interpretation of language. In this paper, we investigate the capabilities of transformer models to perform NLI on cases involving presupposition. First, we present simple heuristics to create alternative “contrastive” test cases based on the ImpPres dataset and investigate the model performance on those test cases. Second, to better understand how the model is making its predictions, we analyze samples from sub-datasets of ImpPres and examine model performance on them. Overall, our findings suggest that NLI-trained transformer models seem to be exploiting specific structural and lexical cues as opposed to performing some kind of pragmatic reasoning.
Re-Examining FactBank: Predicting the Author’s Presentation of Factuality
Authors: John Murzaku, Peter Zeng, Magdalena Markowska, Owen Rambow
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: valid

We present a corrected version of a subset of the FactBank data set. Previously published results on FactBank are no longer valid. We perform experiments on FactBank using multiple training paradigms, data smoothing techniques, and polarity classifiers. We argue that f-measure is an important alternative evaluation metric for factuality. We provide new state-of-the-art results for four corpora including FactBank. We perform an error analysis on Factbank combined with two similar corpora.
The Role of Context and Uncertainty in Shallow Discourse Parsing
Authors: Katherine Atwell, Remi Choi, Junyi Jessy Li, Malihe Alikhani
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Discourse Parsing

Discourse parsing has proven to be useful for a number of NLP tasks that require complex reasoning. However, over a decade since the advent of the Penn Discourse Treebank, predicting implicit discourse relations in text remains challenging. There are several possible reasons for this, and we hypothesize that models should be exposed to more context as it plays an important role in accurate human annotation; meanwhile adding uncertainty measures can improve model accuracy and calibration. To thoroughly investigate this phenomenon, we perform a series of experiments to determine 1) the effects of context on human judgments, and 2) the effect of quantifying uncertainty with annotator confidence ratings on model accuracy and calibration (which we measure using the Brier score (Brier et al, 1950)). We find that including annotator accuracy and confidence improves model accuracy, and incorporating confidence in the model’s temperature function can lead to models with significantly better-calibrated confidence measures. We also find some insightful qualitative results regarding human and model behavior on these datasets.
Improving Commonsense Contingent Reasoning by Pseudo-data and Its Application to the Related Tasks
Authors: Kazumasa Omura, Sadao Kurohashi
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Natural Language Understanding, Relation, Transfer Learning

Contingent reasoning is one of the essential abilities in natural language understanding, and many language resources annotated with contingent relations have been constructed. However, despite the recent advances in deep learning, the task of contingent reasoning is still difficult for computers. In this study, we focus on the reasoning of contingent relation between basic events. Based on the existing data construction method, we automatically generate large-scale pseudo-problems and incorporate the generated data into training. We also investigate the generality of contingent knowledge through quantitative evaluation by performing transfer learning on the related tasks: discourse relation analysis, the Japanese Winograd Schema Challenge, and the JCommonsenseQA. The experimental results show the effectiveness of utilizing pseudo-problems for both the commonsense contingent reasoning task and the related tasks, which suggests the importance of contingent reasoning.
Towards Identifying Alternative-Lexicalization Signals of Discourse Relations
Authors: René Knaebel, Manfred Stede
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Discourse Parsing

The task of shallow discourse parsing in the Penn Discourse Treebank (PDTB) framework has traditionally been restricted to identifying those relations that are signaled by a discourse connective (“explicit”) and those that have no signal at all (“implicit”). The third type, the more flexible group of “AltLex” realizations has been neglected because of its small amount of occurrences in the PDTB2 corpus. Their number has grown significantly in the recent PDTB3, and in this paper, we present the first approaches for recognizing these “alternative lexicalizations”. We compare the performance of a pattern-based approach and a sequence labeling model, add an experiment on the pre-classification of candidate sentences, and provide an initial qualitative analysis of the error cases made by both models.
Topicalization in Language Models: A Case Study on Japanese
Authors: Riki Fujihara, Tatsuki Kuribayashi, Kaori Abe, Ryoko Tokuhisa, Kentaro Inui
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Position, Sentence, Text Generation

Humans use different wordings depending on the context to facilitate efficient communication. For example, instead of completely new information, information related to the preceding context is typically placed at the sentence-initial position. In this study, we analyze whether neural language models (LMs) can capture such discourse-level preferences in text generation. Specifically, we focus on a particular aspect of discourse, namely the topic-comment structure. To analyze the linguistic knowledge of LMs separately, we chose the Japanese language, a topic-prominent language, for designing probing tasks, and we created human topicalization judgment data by crowdsourcing. Our experimental results suggest that LMs have different generalizations from humans; LMs exhibited less context-dependent behaviors toward topicalization judgment. These results highlight the need for the additional inductive biases to guide LMs to achieve successful discourse-level generalization.
“No, They Did Not”: Dialogue Response Dynamics in Pre-trained Language Models
Authors: Sanghee J. Kim, Lang Yu, Allyson Ettinger
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Sensitivity

A critical component of competence in language is being able to identify relevant components of an utterance and reply appropriately. In this paper we examine the extent of such dialogue response sensitivity in pre-trained language models, conducting a series of experiments with a particular focus on sensitivity to dynamics involving phenomena of at-issueness and ellipsis. We find that models show clear sensitivity to a distinctive role of embedded clauses, and a general preference for responses that target main clause content of prior utterances. However, the results indicate mixed and generally weak trends with respect to capturing the full range of dynamics involved in targeting at-issue versus not-at-issue content. Additionally, models show fundamental limitations in grasp of the dynamics governing ellipsis, and response selections show clear interference from superficial factors that outweigh the influence of principled discourse constraints.
New or Old? Exploring How Pre-Trained Language Models Represent Discourse Entities
Authors: Sharid Loáiciga, Anne Beyer, David Schlangen
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Binary Classification, Sentence

Recent research shows that pre-trained language models, built to generate text conditioned on some context, learn to encode syntactic knowledge to a certain degree. This has motivated researchers to move beyond the sentence-level and look into their ability to encode less studied discourse-level phenomena. In this paper, we add to the body of probing research by investigating discourse entity representations in large pre-trained language models in English. Motivated by early theories of discourse and key pieces of previous work, we focus on the information-status of entities as discourse-new or discourse-old. We present two probing models, one based on binary classification and another one on sequence labeling. The results of our experiments show that pre-trained language models do encode information on whether an entity has been introduced before or not in the discourse. However, this information alone is not sufficient to find the entities in a discourse, opening up interesting questions about the definition of entities for future work.
Dialo-AP: A Dependency Parsing Based Argument Parser for Dialogues
Authors: Sougata Saha, Souvik Das, Rohini K. Srihari
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Argument Mining, Dependency Parsing

While neural approaches to argument mining (AM) have advanced considerably, most of the recent work has been limited to parsing monologues. With an urgent interest in the use of conversational agents for broader societal applications, there is a need to advance the state-of-the-art in argument parsers for dialogues. This enables progress towards more purposeful conversations involving persuasion, debate and deliberation. This paper discusses Dialo-AP, an end-to-end argument parser that constructs argument graphs from dialogues. We formulate AM as dependency parsing of elementary and argumentative discourse units; the system is trained using extensive pre-training and curriculum learning comprising nine diverse corpora. Dialo-AP is capable of generating argument graphs from dialogues by performing all sub-tasks of AM. Compared to existing state-of-the-art baselines, Dialo-AP achieves significant improvements across all tasks, which is further validated through rigorous human evaluation.
ConnPrompt: Connective-cloze Prompt Learning for Implicit Discourse Relation Recognition
Authors: Wei Xiang, Zhenglin Wang, Lu Dai, Bang Wang
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Language Modelling, Prompt Learning, Relation, Relation Prediction

Implicit Discourse Relation Recognition (IDRR) is to detect and classify relation sense between two text segments without an explicit connective. Vanilla pre-train and fine-tuning paradigm builds upon a Pre-trained Language Model (PLM) with a task-specific neural network. However, the task objective functions are often not in accordance with that of the PLM. Furthermore, this paradigm cannot well exploit some linguistic evidence embedded in the pre-training process. The recent pre-train, prompt, and predict paradigm selects appropriate prompts to reformulate downstream tasks, so as to utilizing the PLM itself for prediction. However, for its success applications, prompts, verbalizer as well as model training should still be carefully designed for different tasks. As the first trial of using this new paradigm for IDRR, this paper develops a Connective-cloze Prompt (ConnPrompt) to transform the relation prediction task as a connective-cloze task. Specifically, we design two styles of ConnPrompt template: Insert-cloze Prompt (ICP) and Prefix-cloze Prompt (PCP) and construct an answer space mapping to the relation senses based on the hierarchy sense tags and implicit connectives. Furthermore, we use a multi-prompt ensemble to fuse predictions from different prompting results. Experiments on the PDTB corpus show that our method significantly outperforms the state-of-the-art algorithms, even with fewer training data.
A Distance-Aware Multi-Task Framework for Conversational Discourse Parsing
Authors: Yaxin Fan, Peifeng Li, Fang Kong, Qiaoming Zhu
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Discourse Parsing, Multi-Task Learning

Conversational discourse parsing aims to construct an implicit utterance dependency tree to reflect the turn-taking in a multi-party conversation. Existing works are generally divided into two lines: graph-based and transition-based paradigms, which perform well for short-distance and long-distance dependency links, respectively. However, there is no study to consider the advantages of both paradigms to facilitate conversational discourse parsing. As a result, we propose a distance-aware multi-task framework DAMT that incorporates the strengths of transition-based paradigm to facilitate the graph-based paradigm from the encoding and decoding process. To promote multi-task learning on two paradigms, we first introduce an Encoding Interactive Module (EIM) to enhance the flow of semantic information between both two paradigms during the encoding step. And then we apply a Distance-Aware Graph Convolutional Network (DAGCN) in the decoding process, which can incorporate the different-distance dependency links predicted by the transition-based paradigm to facilitate the decoding of the graph-based paradigm. The experimental results on the datasets STAC and Molweni show that our method can significantly improve the performance of the SOTA graph-based paradigm on long-distance dependency links.
Linguistically Motivated Features for Classifying Shorter Text into Fiction and Non-Fiction Genre
Authors: Arman Kazmi, Sidharth Ranjan, Arpit Sharma, Rajakrishnan Rajkumar
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Classification, POS, regression, Sentiment Analysis, TAG, text-classification, Text Classification

This work deploys linguistically motivated features to classify paragraph-level text into fiction and non-fiction genre using a logistic regression model and infers lexical and syntactic properties that distinguish the two genres. Previous works have focused on classifying document-level text into fiction and non-fiction genres, while in this work, we deal with shorter texts which are closer to real-world applications like sentiment analysis of tweets. Going beyond simple POS tag ratios proposed in Qureshi et al.(2019) for document-level classification, we extracted multiple linguistically motivated features belonging to four categories: Lexical features, POS ratio features, Syntactic features and Raw features. For the task of short-text classification, a model containing 28 best-features (selected via Recursive feature elimination with cross-validation; RFECV) confers an accuracy jump of 15.56 % over a baseline model consisting of 2 POS-ratio features found effective in previous work (cited above). The efficacy of the above model containing a linguistically motivated feature set also transfers over to another dataset viz, Baby BNC corpus. We also compared the classification accuracy of the logistic regression model with two deep-learning models. A 1D CNN model gives an increase of 2% accuracy over the logistic Regression classifier on both corpora. And the BERT-base-uncased model gives the best classification accuracy of 97% on Brown corpus and 98% on Baby BNC corpus. Although both the deep learning models give better results in terms of classification accuracy, the problem of interpreting these models remains unsolved. In contrast, regression model coefficients revealed that fiction texts tend to have more character-level diversity and have lower lexical density (quantified using content-function word ratios) compared to non-fiction texts. Moreover, subtle differences in word order exist between the two genres, i.e., in fiction texts Verbs precede Adverbs (inter-alia).
Semantic Sentence Matching via Interacting Syntax Graphs
Authors: Chen Xu, Jun Xu, Zhenhua Dong, Ji-Rong Wen
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Graph Matching, Sentence

Studies have shown that the sentence’s syntactic structures are important for semantic sentence matching. A typical approach is encoding each sentence’s syntactic structure into an embedding vector, which can be combined with other features to predict the final matching scores. Though successes have been observed, embedding the whole syntactic structures as one vector inevitably overlooks the fine-grained syntax matching patterns, e.g. the alignment of specific term dependencies relations in the two inputted sentences. In this paper, we formalize the task of semantic sentence matching as a problem of graph matching in which each sentence is represented as a directed graph according to its syntactic structures. The syntax matching patterns (i.e. similar syntactic structures) between two sentences, therefore, can be extracted as the sub-graph structure alignments. The proposed method, referred to as Interacted Syntax Graphs (ISG), represents two sentences’ syntactic alignments as well as their semantic matching signals into one association graph. After that, the neural quadratic assignment programming (QAP) is adapted to extract syntactic matching patterns from the association graph. In this way, the syntactic structures fully interact in a fine granularity during the matching process. Experimental results on three public datasets demonstrated that ISG can outperform the state-of-the-art baselines effectively and efficiently. The empirical analysis also showed that ISG can match sentences in an interpretable way.
Community Topic: Topic Model Inference by Consecutive Word Community Discovery
Authors: Eric Austin, Osmar R. Zaïane, Christine Largeron
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10

We present our novel, hyperparameter-free topic modelling algorithm, Community Topic. Our algorithm is based on mining communities from term co-occurrence networks. We empirically evaluate and compare Community Topic with Latent Dirichlet Allocation and the recently developed top2vec algorithm. We find that Community Topic runs faster than the competitors and produces topics that achieve higher coherence scores. Community Topic can discover coherent topics at various scales. The network representation used by Community Topic results in a natural relationship between topics and a topic hierarchy. This allows sub- and super-topics to be found on demand. These features make Community Topic the ideal tool for downstream applications such as applied research and conversational agents.