pwc-archive/papers-with-abstracts huggingface dataset:
Fine-grained video paragraph captioning via exploring object-centered internal and external knowledge
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Sentence

Video paragraph captioning task aims at generating a fine-grained, coherent and relevant paragraph for a video. Existing works often treat the objects (the potential main components in a sentence) isolated from the whole video content, and rarely explore the latent semantic relation between a certain object and the current video concepts, causing the generated description dull and even incorrect. Besides, different from images where objects are static, the temporal states of objects are changing in videos. The dynamic information could be contributed to better understand the whole video content. Towards generating a more detailed and stick-to-the-topic paragraph, we propose a novel framework that focuses on exploring the rich semantic and temporal meaning of objects, by constructing the concept graph from the external commonsense knowledge and the state graph from the internal video frames. Extensive experiments on ActivityNet captions and Youcook2 demonstrate the effectiveness of our method compared to the state-of-the-art works. We will release our code on GitHub community.
Counselling Summarization using Mental Health Knowledge guided Utterance Filtering
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11

The psychotherapy intervention technique is a multifaceted conversation between a therapist and a patient. Unlike general clinical discussions, psychotherapy's core components (viz. symptoms) are hard to diagnose and become a complex problem to summarize later. A structured counselling conversation may contain discussions on symptoms or reasons for mental health issues, the discovery of the patient's behaviour and habits, and the rest of the conversation is generally chit-chat. We call these psychotherapy ingredients to be counselling components. We aim for the task of mental health counselling summarization exploiting domain knowledge. We curated a new dataset, MEMO, where we annotate 12.9K utterances for counselling components and reference summaries for each dialogue. Further, we propose ConSum: a novel counselling-component guided summarization model. The model works in three independent modules. First, it uses mental health domain knowledge to filter utterances by utilising the Patient Health Questionnaire (PHQ-9), while, the second and third modules work on the classification of counselling components. At last, we propose a problem specific Mental Health Information Capture (MHIC) evaluation metric for counselling summaries. Our extensive ablation study shows that we improve on the scores and better language semantics. We comprehensively analyse the generated summaries to investigate the capturing of core components of psychotherapy. In last, we discuss the uniqueness in mental health counselling summarization in contrast with medical conversation.
Automode: Choosing the Best Machine Translation Supplier Based on Source Text
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Machine Translation, regression, Translation

This study presents Automode, a framework consisting of two quality estimators which, within the wide-range of options available nowadays, aims to automatically select the MT supplier which is more likely to provide a good translation to a source text in terms of fluency and mitigation of gender bias. Among six analysed MT suppliers, results show that a simple machine learning regression can learn how to highly score the supplier which is more likely to generate a fluent translation to a source text, while reducing the translation cost compared to the best performing single supplier in some scenarios. However, for gender-bias we noticed that an open-source supplier is the one which generates the less biased translations regarding gender.
Exploring Human-judged and Automatically-induced Correction Difficulty for Grammatical Error Correction
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Grammatical Error Correction

While grammatical error correction (GEC) has improved in its correction performance, one of the key challenges in GEC research still remains in evaluation. Specifically, all errors are equally treated in the conventional performance measures despite the fact that some errors are more difficult to correct than others. Ideally, difficult errors should be regarded to be more important than easy ones in evaluation. This leads to the following ultimate research question --- Can even human experts estimate correction difficulty well? In this paper, we explore questions about correction difficulty centering on this research question. For this purpose, we first introduce a method for estimating agreement rates in correction difficulty judgements based on pairwise comparison. With the annotation of 2,025 instances using this method, we show that human experts exhibit a moderate agreement rate of 66.39\% (Cohen's-$\kappa$: 0.42) in judging correction difficulty. We also show that the agreement between this human-based difficulty and an automatically induced difficulty is comparable (64.50\% and $\kappa=0.35$ on average). We further look into the annotation results to reveal the insights of the human-judged and machine-judged correction difficulties, reporting on following three findings: (i) where the human-judged and machine-judged difficulties are strong and weak; (ii) based on (i), correction difficulty can be GEC-algorithm- and training-corpus-dependent; (iii) human-judged and machine-judged correction difficulties complement each other.
A Model-agnostic Data Manipulation Method for Persona-based Dialogue Generation
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Decoder, Dialogue Generation

Towards building intelligent dialogue agents, there has been a growing interest in introducing explicit personas in generation models. However, with limited persona-based dialogue data at hand, it may be difficult to train a dialogue generation model well. We point out that the data challenges of this generation task lie in two aspects: first, it is expensive to scale up current persona-based dialogue datasets; second, each data sample in this task is more complex to learn with than conventional dialogue data. To alleviate the above data issues, we propose a data manipulation method, which is model-agnostic to be packed with any persona-based dialogue generation model to improve their performance. The original training samples will first be distilled and thus expected to be fitted more easily. Next, we show various effective ways that can diversify such easier distilled data. A given base model will then be trained via the constructed data curricula, i.e. first on augmented distilled samples and then on original ones. Experiments illustrate the superiority of our method with two strong base dialogue models (Transformer encoder-decoder and GPT2).
Life after BERT: What do Other Muppets Understand about Language?
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11

Pre-trained transformers are at the core of natural language processing today. However, the understanding of what model learns during pre-training is still limited. Existing model analysis works usually focus only on one or two model families at a time, overlooking the variety of existing architectures and pre-training objectives. In our work, we utilize the oLMpics benchmark and psycholinguistic probing datasets for a diverse set of 28 models including T5, BART, and ALBERT. Additionally, we adapt the oLMpics zero-shot setup for autoregressive models and evaluate GPT networks of different sizes. Our findings show that none of these models can resolve compositional questions in a zero-shot fashion, suggesting that this skill is not learnable using existing pre-training objectives. Additionally, we find that global model decisions such as architecture, directionality, size of the dataset, and pre-training objective are not predictive of a model's linguistic capabilities.
Moving the Eiffel Tower to ROME: Tracing and Editing Facts in GPT
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: knowledge editing, Model Editing, Specificity

We investigate the mechanisms underlying factual knowledge recall in auto-regressive transformer language models. To this end, we develop a method for identifying neuron activations that are capable of altering a model's factual predictions. Within GPT-2, this reveals two distinct sets of neurons that we hypothesize correspond to knowing an abstract fact and saying a concrete word, respectively. Based on this insight, we propose ROME, a simple and efficient rank-one model editing method for rewriting abstract facts in auto-regressive language models. For validation, we introduce CounterFact, a dataset of over twenty thousand rewritable facts, as well as tools to facilitate sensitive measurements of edit quality. Compared to previously-published knowledge editing methods, ROME achieves superior generalization and specificity.
Dataset for N-ary Relation Extraction of Drug Combinations
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Relation, Relation Extraction, Sentence

Combination therapies have become the standard of care for diseases such as cancer, tuberculosis, malaria and HIV. However, the combinatorial set of available multi-drug treatments creates a challenge, particularly in the presence of antagonistic drug combinations that may lead to negative patient outcomes. To assist medical professionals in identifying beneficial drug-combinations, we construct an expert-annotated dataset for extracting information about the efficacy of drug combinations from the scientific literature. Beyond its practical utility, the dataset also presents a unique NLP challenge, as it is the first relation extraction dataset consisting of variable-length relations. Furthermore, the relations in this dataset predominantly require language understanding beyond the sentence level, adding to the challenge of this task. We provide a strong baseline model and identify clear areas for further improvement. We release our dataset and code (https://anonymous.4open.science/r/drug-synergy-models--C8B7/README.md) publicly to encourage the NLP community to participate in this task.
MSP: Multi-Stage Prompting for Making Pre-trained Language Models Better Translators
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Translation

Prompting has recently been shown as a promising approach for applying pre-trained language models to perform downstream tasks. We present Multi-Stage Prompting, a simple and automatic approach for leveraging pre-trained language models to translation tasks. To better mitigate the discrepancy between pre-training and translation, MSP divides the translation process via pre-trained language models into three separate stages: the encoding stage, the re-encoding stage, and the decoding stage. During each stage, we independently apply different continuous prompts for allowing pre-trained language models better shift to translation tasks. We conduct extensive experiments on three translation tasks. Experiments show that our method can significantly improve the translation performance of pre-trained language models.
EmoWOZ: A Large-Scale Corpus and Labelling Scheme for Emotion Recognition in Task-Oriented Dialogue Systems
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Emotion Recognition, Task-Oriented Dialogue Systems

The ability to recognise emotions lends a conversational artificial intelligence a human touch. While emotions in chit-chat dialogues have received substantial attention, emotions in task-oriented dialogues have been largely overlooked despite having an equally important role, such as to signal failure or success. Existing emotion-annotated task-oriented corpora are limited in size, label richness, and public availability, creating a bottleneck for downstream tasks. To lay a foundation for studies on emotions in task-oriented dialogues, we introduce EmoWOZ, a large-scale manually emotion-annotated corpus of task-oriented dialogues. EmoWOZ is based on MultiWOZ, a multi-domain task-oriented dialogue dataset. It contains more than 11K dialogues with more than 83K emotion annotations of user utterances. In addition to Wizard-of-Oz dialogues from MultiWOZ, we collect human-machine dialogues within the same set of domains to sufficiently cover the space of various emotions that can happen during the lifetime of a data-driven dialogue system. To the best of our knowledge, this is the first large-scale open-source corpus of its kind. We propose a novel emotion labelling scheme, which is tailored to task-oriented dialogues. We report a set of experimental results to show the usability of this corpus for emotion recognition and state tracking in task-oriented dialogues.
EveMRC: A Two-stage Evidence Modeling For Multi-choice Machine Reading Comprehension
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Data Augmentation, Machine Reading Comprehension, Multi-Choice MRC, Reading Comprehension

Many impressive works have been proposed to improve the performance of Machine Reading Comprehension (MRC) systems in recent years. However, it is still difficult to interpret the predictions of existing MRC models, which makes the predictions unconvincing.
In this work, we propose a two-stage explainable framework for multi-choice MRC to model not only the correlation between answers and evidence, but also the competition among evidence. In stage 1, we select evidence sentences for both the right answer and wrong answers using the semi-supervised evidence selector. In stage 2, we employ an evidence discriminator to compare among the competitive evidence set and make final judgments. Moreover, we propose an evidence-enabled data augmentation method.  Experiments on four multi-choice MRC datasets show that: stage 1 provides strong explainability for MRC systems and stage 2 improves both the performance and robustness of MRC systems meanwhile.
Bidirectional Modeling for Simultaneous Neural Machine Translation
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Machine Translation, Sentence, Translation

Simultaneous Neural Machine Translation (SimulNMT) generates the output before the entire input sentence is available and only uses the unidirectional attention from left-to-right so that its decoding highly relies on future forecast according to word ordering rules. However, it is utopian that the word order strictly obeys the grammar rules in a language, especially in oral. To address the mismatch between SimulNMT expecting strict word order and free word order in real scenario, we propose a bidirectional modeling. In detail, we train another backward model where the input sentence is from right-to-left and keep the target sentence from left-to-right. Then we join this backward model into the standard forward SimulNMT model during decoding. This strategy enhances the robustness of SimulNMT and empowers the model to be more adaptable for the inconstant word ordering phenomenon. Experiments show that our method brings improvement over the strong baselines.
Generation of News Articles from Tweets : An Experiment
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Abstractive Text Summarization, Articles

With millions of users, Twitter is among the most popular social media platforms today, making it one of the largest sources of continuous real-time information on the Internet. Users frequently broadcast events occurring across the world on this platform, providing a scope for the production of news articles by aggregating event-specific tweets. This paper contributes to this task and evaluates the existing state-of-the-art abstractive summarization models to see how efficient or capable they are when it comes to the task of generating news articles from tweets. We compare summaries generated by five such models qualitatively in order to see how concise or coherent they are, whether they can capture the important facts of the entire tweet dataset relating to a topic and how similar they are to form news articles written by humans. PEGASUS (Pre-training with Extracted Gap-Sentences for Abstractive SUmmarization Sequence-to-sequence models) pretrained with CNN/Daily mail dataset outperformed over T5 (Text-to-Text Transfer Transformer). Manual evaluation shows that the generated summaries are concise and cohesive.
Speaker Information Can Guide Models to Better Inductive Biases: A Case Study On Predicting Code-Switching
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11

Natural language processing (NLP) models trained on people-generated data can be unreliable because, without any constraints, they can learn from spurious correlations or propagate dangerous biases about personal identities. We hypothesize that enriching models with speaker information in a controlled, educated way can guide them to pick up on relevant inductive biases. For the speaker-driven task of predicting code-switching points in English--Spanish bilingual dialogues, we show that adding sociolinguistically-grounded speaker features as prepended prompts significantly helps to improve accuracy.  We find that by adding influential phrases to the input, speaker-informed models learn useful and explainable linguistic information. To our knowledge, we are the first to incorporate speaker characteristics in the code-switching setup, and more generally, take a step towards developing transparent models that control for biases in person-centric tasks.
Unsupervised Multi-Granularity Summarization
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Abstractive Text Summarization, Text Summarization

Text summarization is a user-preference based task. For one document, users often have different priorities for summary. Granularity level of the summary is a core component of these preferences. However, most existing studies focus solely on single-granularity scenarios, resulting in models that are limited to producing summaries with similar semantic coverage and are not customizable. In this paper, we propose the first unsupervised multi-granularity summarization framework, GranuSum. We regard events as basic semantic units of the original text and design a model that can take these events as anchors when generating summary. Meanwhile, by ranking these hint events and controlling the number of events, GranuSum is capable of generating summaries at different granularities in an unsupervised manner. We develop a testbed for the multi-granularity summarization task, including a new human-annotated benchmark GranuDUC where each document is paired with multiple summaries with different granularities. Extensive experiments on this benchmark and other large-scale datasets show that GranuSum substantially outperforms previous baselines. We also find that GranuSum exhibits impressive performance on conventional unsupervised abstractive summarization tasks via exploiting the event information, achieving new state-of-the-art results on three summarization datasets.
Explicit Object Relation Alignment for Vision and Language Navigation
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Instruction Following, Relation, Spatial Reasoning, Vision and Language Navigation

We propose a neural agent to solve the navigation instruction following problem in a photo-realistic environment. We explicitly align the spatial information in both instruction and the visual environment, including landmarks and spatial relationships between the agent and landmarks. Our method significantly improves the baseline and is competitive with the SOTA in unseen environments. The qualitative analysis shows that explicitly modeled spatial reasoning improves the explainability of the action decisions and the generalizability of the model.
Text Smoothing: Enhance Various Data Augmentation Methods on Text Classification Tasks
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Data Augmentation, Language Modeling, Language Modelling, Sentence, text-classification, Text Classification

Before entering the neural network, a token needs to be converted to its one-hot representation, which is a discrete distribution of the vocabulary. Smoothed representation is the probability of candidate tokens obtained from the pre-trained masked language model, which can be seen as a more informative augmented substitution to the one-hot representation. We propose an efficient data augmentation method,  dub as text smoothing, by converting a sentence from its one-hot representation to controllable smoothed representation.
We evaluate text smoothing on different datasets in a low-resource regime. Experimental results show that text smoothing outperforms various mainstream data augmentation methods by a substantial margin. Moreover, text smoothing can be combined with these data augmentation methods to achieve better performance.
Nested Named Entity Recognition as Latent Lexicalized Constituency Parsing
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Constituency Parsing, Entity Typing, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Nested Named Entity Recognition

Nested named entity recognition (NER) has been receiving increasing attention. Recently, Fu et al. (2020) adapt a span-based constituency parser to tackle nested NER. They treat nested entities as partially-observed constituency trees and propose the masked inside algorithm for partial marginalization. However, their method cannot leverage entity heads, which have been shown useful in entity mention detection and entity typing. In this work, we resort to more expressive structures, lexicalized constituency trees in which constituents are annotated by headwords, to model nested entities. We leverage the Eisner-Satta algorithm to perform partial marginalization and inference efficiently.
In addition, we propose to use (1) a two-stage strategy (2) a head regularization loss and (3) a head-aware labeling loss in order to enhance the performance. We make a thorough ablation study to investigate the functionality of each component. Experimentally, our method achieves the state-of-the-art performance on ACE2004, ACE2005 and NNE, and competitive performance on GENIA, and meanwhile has a fast inference speed.