ymoslem/acl-abstracts huggingface dataset:
On Search Strategies for Document-Level Neural Machine Translation (2023)

Compared to sentence-level systems, document-level neural machine translation (NMT) models produce a more consistent output across a document and are able to better resolve ambiguities within the input. There are many works on document-level NMT, mostly focusing on modifying the model architecture or training strategy to better accommodate the additional context-input. On the other hand, in most works, the question on how to perform search with the trained model is scarcely discussed, sometimes not mentioned at all. In this work, we aim to answer the question how to best utilize a context-aware translation model in decoding. We start with the most popular document-level NMT approach and compare different decoding schemes, some from the literature and others proposed by us. In the comparison, we are using both, standard automatic metrics, as well as specific linguistic phenomena on three standard document-level translation benchmarks. We find that most commonly used decoding strategies perform similar to each other and that higher quality context information has the potential to further improve the translation.
Causal Intervention for Mitigating Name Bias in Machine Reading Comprehension (2023)

Machine Reading Comprehension (MRC) is to answer questions based on a given passage, which has made great achievements using pre-trained Language Models (LMs). We study the robustness of MRC models to names which is flexible and repeatability. MRC models based on LMs may overuse the name information to make predictions, which causes the representation of names to be non-interchangeable, called name bias. In this paper, we propose a novel Causal Interventional paradigm for MRC (CI4MRC) to mitigate name bias. Specifically, we uncover that the pre-trained knowledge concerning names is indeed a confounder by analyzing the causalities among the pre-trained knowledge, context representation and answers based on a Structural Causal Model (SCM). We develop effective CI4MRC algorithmic implementations to constrain the confounder based on the neuron-wise and token-wise adjustments. Experiments demonstrate that our proposed CI4MRC effectively mitigates the name bias and achieves competitive performance on the original SQuAD. Moreover, our method is general to various pre-trained LMs and performs robustly on the adversarial datasets.
Counterfactual Probing for the Influence of Affect and Specificity on Intergroup Bias (2023)

While existing work on studying bias in NLP focues on negative or pejorative language use, Govindarajan et al. (2023) offer a revised framing of bias in terms of intergroup social context, and its effects on language behavior. In this paper, we investigate if two pragmatic features (specificity and affect) systematically vary in different intergroup contexts {---} thus connecting this new framing of bias to language output. Preliminary analysis finds modest correlations between specificity and affect of tweets with supervised intergroup relationship (IGR) labels. Counterfactual probing further reveals that while neural models finetuned for predicting IGR reliably use affect in classification, the model`s usage of specificity is inconclusive.
SongRewriter: A Chinese Song Rewriting System with Controllable Content and Rhyme Scheme (2023)

Although lyrics generation has achieved significant progress in recent years, it has limited practical applications because the generated lyrics cannot be performed without composing compatible melodies. In this work, we bridge this practical gap by proposing a song rewriting system which rewrites the lyrics of an existing song such that the generated lyrics are compatible with the rhythm of the existing melody and thus singable. In particular, we propose SongRewriter, a controllable Chinese lyric generation and editing system which assists users without prior knowledge of melody composition. The system is trained by a randomized multi-level masking strategy which produces a unified model for generating entirely new lyrics or editing a few fragments. To improve the controllabiliy of the generation process, we further incorporate a keyword prompt to control the lexical choices of the content and propose novel decoding constraints and a vowel modeling task to enable flexible end and internal rhyme schemes. While prior rhyming metrics are mainly for rap lyrics, we propose three novel rhyming evaluation metrics for song lyrics. Both automatic and human evaluations show that the proposed model performs better than the state-of-the-art models in both contents and rhyming quality.
Triplet-Free Knowledge-Guided Response Generation (2023)

Generating vivid and informative responses (e.g., comments for social posts and utterances for dialogues) is challenging without giving relevant knowledge. Prior works focus on constructing the {\textquotedblright}latent{\textquotedblright} knowledge first and then learning how to {\textquotedblright}ground{\textquotedblright} it based on pseudo (context, knowledge, response) triplets. However, the retrieval between real responses and their latent knowledge is difficult in nature. In this paper, instead of focusing on how to ground knowledge given the responses, we take a different perspective to optimize the final responses for given guided knowledge directly. This allows us to re-formulate the entire problem in a simplified yet more scalable way. Specifically, we pretrain a response language model (LM) to measure the relevance and consistency between any context and response, then use search engines to collect the top-ranked passages to serve as the guiding knowledge without explicitly optimizing the {\textquoteleft}{\textquoteleft}best{\textquotedblright} latent knowledge that corresponds to a given response. The final response generation model is trained through reinforcement learning by taking both the response LM prior and knowledge-injection rate as rewards. For better evaluations, we construct a new Chinese benchmark, {\textquotedblright}IceKC{\textquotedblright}, using fresh multimodal online social posts. Both automatic evaluations and human evaluations show our zero-resource approach performs significantly better than prior works.
Implicit Memory Transformer for Computationally Efficient Simultaneous Speech Translation (2023)

Simultaneous speech translation is an essential communication task difficult for humans whereby a translation is generated concurrently with oncoming speech inputs. For such a streaming task, transformers using block processing to break an input sequence into segments have achieved state-of-the-art performance at a reduced cost. Current methods to allow information to propagate across segments, including left context and memory banks, have faltered as they are both insufficient representations and unnecessarily expensive to compute. In this paper, we propose an Implicit Memory Transformer that implicitly retains memory through a new left context method, removing the need to explicitly represent memory with memory banks. We generate the left context from the attention output of the previous segment and include it in the keys and values of the current segment`s attention calculation. Experiments on the MuST-C dataset show that the Implicit Memory Transformer provides a substantial speedup on the encoder forward pass with nearly identical translation quality when compared with the state-of-the-art approach that employs both left context and memory banks.
Enhancing Document-level Event Argument Extraction with Contextual Clues and Role Relevance (2023)

Document-level event argument extraction poses new challenges of long input and cross-sentence inference compared to its sentence-level counterpart. However, most prior works focus on capturing the relations between candidate arguments and the event trigger in each event, ignoring two crucial points: a) non-argument contextual clue information; b) the relevance among argument roles. In this paper, we propose a SCPRG (Span-trigger-based Contextual Pooling and latent Role Guidance) model, which contains two novel and effective modules for the above problem. The Span-Trigger-based Contextual Pooling (STCP) adaptively selects and aggregates the information of non-argument clue words based on the context attention weights of specific argument-trigger pairs from pre-trained model. The Role-based Latent Information Guidance (RLIG) module constructs latent role representations, makes them interact through role-interactive encoding to capture semantic relevance, and merges them into candidate arguments. Both STCP and RLIG introduce no more than 1{\%} new parameters compared with the base model and can be easily applied to other event extraction models, which are compact and transplantable. Experiments on two public datasets show that our SCPRG outperforms previous state-of-the-art methods, with 1.13 F1 and 2.64 F1 improvements on RAMS and WikiEvents respectively. Further analyses illustrate the interpretability of our model.
Exploring the Impact of Vision Features in News Image Captioning (2023)

The task of news image captioning aims to generate a detailed caption which describes the specific information of an image in a news article. However, we find that recent state-of-art models can achieve competitive performance even without vision features. To resolve the impact of vision features in the news image captioning task, we conduct extensive experiments with mainstream models based on encoder-decoder framework. From our exploration, we find 1) vision features do contribute to the generation of news image captions; 2) vision features can assist models to better generate entities of captions when the entity information is sufficient in the input textual context of the given article; 3) Regions of specific objects in images contribute to the generation of related entities in captions.
Using Collostructional Analysis to evaluate BERT`s representation of linguistic constructions (2023)

Collostructional analysis is a technique devised to find correlations between particular words and linguistic constructions in order to analyse meaning associations of these constructions. Contrasting collostructional analysis results with output from BERT might provide insights into the way BERT represents the meaning of linguistic constructions. This study tests to what extent English BERT`s meaning representations correspond to known constructions from the linguistics literature by means of two tasks that we propose. Firstly, by predicting the words that can be used in open slots of constructions, the meaning associations of more lexicalized constructions can be observed. Secondly, by finding similar sequences using BERT`s output embeddings and manually reviewing the resulting sentences, we can observe whether instances of less lexicalized constructions are clustered together in semantic space. These two methods show that BERT represents constructional meaning to a certain extent, but does not separate instances of a construction from a near-synonymous construction that has a different form.
Selecting Better Samples from Pre-trained LLMs: A Case Study on Question Generation (2023)

Large Language Models (LLMs) have in recent years demonstrated impressive prowess in natural language generation. A common practice to improve generation diversity is to sample multiple outputs from the model. However, partly due to the inaccessibility of LLMs, there lacks a simple and robust way of selecting the best output from these stochastic samples. As a case study framed in the context of question generation, we propose two prompt-based approaches, namely round-trip and prompt-based score, to selecting high-quality questions from a set of LLM-generated candidates. Our method works without the need to modify the underlying model, nor does it rely on human-annotated references {---} both of which are realistic constraints for real-world deployment of LLMs. With automatic as well as human evaluations, we empirically demonstrate that our approach can effectively select questions of higher qualities than greedy generation.
Sentiment Knowledge Enhanced Self-supervised Learning for Multimodal Sentiment Analysis (2023)

Multimodal Sentiment Analysis (MSA) has made great progress that benefits from extraordinary fusion scheme. However, there is a lack of labeled data, resulting in severe overfitting and poor generalization for supervised models applied in this field. In this paper, we propose Sentiment Knowledge Enhanced Self-supervised Learning (SKESL) to capture common sentimental patterns in unlabeled videos, which facilitates further learning on limited labeled data. Specifically, with the help of sentiment knowledge and non-verbal behavior, SKESL conducts sentiment word masking and predicts fine-grained word sentiment intensity, so as to embed sentiment information at the word level into pre-trained multimodal representation. In addition, a non-verbal injection method is also proposed to integrate non-verbal information into the word semantics. Experiments on two standard benchmarks of MSA clearly show that SKESL significantly outperforms the baseline, and achieves new State-Of-The-Art (SOTA) results.
Theory of Mind in Freely-Told Children`s Narratives: A Classification Approach (2023)

Children are the focal point for studying the link between language and Theory of Mind (ToM) competence. Language and ToM are often studied with younger children and standardized tests, but as both are social competences, data and methods with higher ecological validity are critical. We leverage a corpus of 442 freely-told stories by Dutch children aged 4-12, recorded in their everyday classroom environments, to study language and ToM with NLP-tools. We labelled stories according to the mental depth of story characters children create, as a proxy for their ToM competence {\textquoteleft}in action', and built a classifier with features encoding linguistic competences identified in existing work as predictive of ToM.We obtain good and fairly robust results (F1-macro = .71), relative to the complexity of the task for humans. Our results are explainable in that we link specific linguistic features such as lexical complexity and sentential complementation, that are relatively independent of children`s ages, to higher levels of character depth. This confirms and extends earlier work, as our study includes older children and socially embedded data from a different domain. Overall, our results support the idea that language and ToM are strongly interlinked, and that in narratives the former can scaffold the latter.
Better Language Models of Code through Self-Improvement (2023)

Pre-trained language models for code (PLMCs) have gained attention in recent research. These models are pre-trained on large-scale datasets using multi-modal objectives. However, fine-tuning them requires extensive supervision and is limited by the size of the dataset provided. We aim to improve this issue by proposing a data augmentation framework using knowledge distillation. Our framework utilizes knowledge gained during the pre-training and fine-tuning stage to augment training data, which is then used for the next step. We incorporate this framework into the state-of-the-art language models, such as CodeT5, CodeBERT, and UnixCoder. The results show that our framework significantly improves PLMCs' performance in sequence-generation tasks, such as code summarization and code generation in the CodeXGLUE benchmark.
Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them (2023)

BIG-Bench (Srivastava et al., 2022) is a diverse evaluation suite that focuses on tasks believed to be beyond the capabilities of current language models. Language models have already made good progress on this benchmark, with the best model in the BIG-Bench paper outperforming average reported human-rater results on 65{\%} of the BIG-Bench tasks via few-shot prompting. But on what tasks do language models fall short of average human-rater performance, and are those tasks actually unsolvable by current language models? In this work, we focus on a suite of 23 challenging BIG-Bench tasks which we call BIG-Bench Hard (BBH). These are the tasks for which prior language model evaluations did not outperform the average human-rater. We find that applying chain-of-thought (CoT) prompting to BBH tasks enables PaLM to surpass the average human-rater performance on 10 of the 23 tasks, and Codex (code-davinci-002) to surpass the average human-rater performance on 17 of the 23 tasks. Since many tasks in BBH require multi-step reasoning, few-shot prompting without CoT, as done in the BIG-Bench evaluations (Srivastava et al., 2022), substantially underestimates the best performance and capabilities of language models, which is better captured via CoT prompting. As further analysis, we explore the interaction between CoT and model scale on BBH, finding that CoT enables emergent task performance on several BBH tasks with otherwise flat scaling curves.
Score It All Together: A Multi-Task Learning Study on Automatic Scoring of Argumentative Essays (2023)

When scoring argumentative essays in an educational context, not only the presence or absence of certain argumentative elements but also their quality is important. On the recently published student essay dataset PERSUADE, we first show that the automatic scoring of argument quality benefits from additional information about context, writing prompt and argument type. We then explore the different combinations of three tasks: automated span detection, type and quality prediction. Results show that a multi-task learning approach combining the three tasks outperforms sequential approaches that first learn to segment and then predict the quality/type of a segment.
Data Sampling and (In)stability in Machine Translation Evaluation (2023)

We analyze the different data sampling approaches used in selecting data for human evaluation and ranking of machine translation systems at the highly influential Conference on Machine Translation (WMT). By using automatic evaluation metrics, we are able to focus on the impact of the data sampling procedure as separate from questions about human annotator consistency. We provide evidence that the latest data sampling approach used at WMT skews the annotated data toward shorter documents, not necessarily representative of the full test set. Lastly, we examine a new data sampling method that uses the available labour budget to sample data in a more representative manner, with the goals of improving representation of various document lengths in the sample and producing more stable rankings of system translation quality.
Probing Graph Decomposition for Argument Pair Extraction (2023)

Argument pair extraction (APE) aims to extract interactive argument pairs from two passages within a discussion. The key challenge of APE is to effectively capture the complex context-aware interactive relations of arguments between the two passages. In this paper, we elicit relational semantic knowledge from large-scale pre-trained language models (PLMs) via a probing technique. The induced sentence-level relational probing graph can help capture rich explicit interactive relations between argument pairs effectively. Since the relevance score of a sentence pair within a passage is generally larger than that of the sentence pair from different passages, each sentence would prefer to propagate information within the same passage and under-explore the interactive relations between two passages. To tackle this issue, we propose a graph decomposition method to decompose the probing graph into four sub-graphs from intra- and inter-passage perspectives, where the intra-passage graphs can help detect argument spans within each passage and the inter-passage graphs can help identify the argument pairs between the review and rebuttal passages. Experimental results on two benchmark datasets show that our method achieves substantial improvements over strong baselines for APE.
DiffuSum: Generation Enhanced Extractive Summarization with Diffusion (2023)

Extractive summarization aims to form a summary by directly extracting sentences from the source document. Existing works mostly formulate it as a sequence labeling problem by making individual sentence label predictions. This paper proposes DiffuSum, a novel paradigm for extractive summarization, by directly generating the desired summary sentence representations with diffusion models and extracting sentences based on sentence representation matching. In addition, DiffuSum jointly optimizes a contrastive sentence encoder with a matching loss for sentence representation alignment and a multi-class contrastive loss for representation diversity. Experimental results show that DiffuSum achieves the new state-of-the-art extractive results on CNN/DailyMail with ROUGE scores of 44.83/22.56/40.56. Experiments on the other two datasets with different summary lengths and cross-dataset evaluation also demonstrate the effectiveness of DiffuSum. The strong performance of our framework shows the great potential of adapting generative models for extractive summarization.
Towards Parameter-Efficient Integration of Pre-Trained Language Models In Temporal Video Grounding (2023)

This paper explores the task of Temporal Video Grounding (TVG) where, given an untrimmed video and a query sentence, the goal is to recognize and determine temporal boundaries of action instances in the video described by natural language queries. Recent works tackled this task by improving query inputs with large pre-trained language models (PLM), at the cost of more expensive training. However, the effects of this integration are unclear, as these works also propose improvements in the visual inputs. Therefore, this paper studies the role of query sentence representation with PLMs in TVG and assesses the applicability of parameter-efficient training with NLP adapters. We couple popular PLMs with a selection of existing approaches and test different adapters to reduce the impact of the additional parameters. Our results on three challenging datasets show that, with the same visual inputs, TVG models greatly benefited from the PLM integration and fine-tuning, stressing the importance of the text query representation in this task. Furthermore, adapters were an effective alternative to full fine-tuning, even though they are not tailored to our task, allowing PLM integration in larger TVG models and delivering results comparable to SOTA models. Finally, our results shed light on which adapters work best in different scenarios.
A Memory Model for Question Answering from Streaming Data Supported by Rehearsal and Anticipation of Coreference Information (2023)

Existing question answering methods often assume that the input content (e.g., documents or videos) is always accessible to solve the task. Alternatively, memory networks were introduced to mimic the human process of incremental comprehension and compression of the information in a fixed-capacity memory. However, these models only learn how to maintain memory by backpropagating errors in the answers through the entire network. Instead, it has been suggested that humans have effective mechanisms to boost their memorization capacities, such as rehearsal and anticipation. Drawing inspiration from these, we propose a memory model that performs rehearsal and anticipation while processing inputs to memorize important information for solving question answering tasks from streaming data. The proposed mechanisms are applied self-supervised during training through masked modeling tasks focused on coreference information. We validate our model on a short-sequence (bAbI) dataset as well as large-sequence textual (NarrativeQA) and video (ActivityNet-QA) question answering datasets, where it achieves substantial improvements over previous memory network approaches. Furthermore, our ablation study confirms the proposed mechanisms' importance for memory models.