The cascaded approach continues to be the most popular choice for speech translation (ST).This approach consists of an automatic speech recognition (ASR) model and a machine translation (MT) model that are used in a pipeline to translate speech in one language to text in another language.MT models are often trained on well-formed text and therefore lack robustness while translating noisy ASR outputs in the cascaded approach, degrading the overall translation quality significantly.We address this robustness problem in downstream MT models by forcing the MT encoder to bring the representations of a noisy input closer to its clean version in the semantic space.This is achieved by introducing a contrastive learning method that leverages adversarial examples in the form of ASR outputs paired with their corresponding human transcripts to optimize the network parameters.In addition, a curriculum learning strategy is then used to stabilize the training by alternating the standard MT log-likelihood loss and the contrastive losses.Our approach achieves significant gains of up to 3 BLEU scores in English-German and English-French speech translation without hurting the translation quality on clean text.

Large language models (LLMs) like ChatGPT can be expensive to train, deploy, and use for specific natural language generation tasks such as text summarization and for certain domains.A promising alternative is to fine-tune relatively smaller language models (LMs) on a particular task using high-quality, in-domain datasets.However, it can be prohibitively expensive to get such high-quality training data.This issue has been mitigated by generating weakly supervised data via knowledge distillation (KD) of LLMs.We propose a three-step approach to distill ChatGPT and fine-tune smaller LMs for summarizing forum conversations.More specifically, we design a method to selectively sample a large unannotated corpus of forum conversation using a semantic similarity metric.Then, we use the same metric to retrieve suitable prompts for ChatGPT from a small annotated validation set in the same domain.The generated dataset is then filtered to remove lowquality instances.Our proposed select-promptfilter KD approach leads to significant improvements of up to 6.6 ROUGE-2 score by leveraging sufficient in-domain pseudo-labelled data, over a standard KD approach given the same size of training data.

Non-Sentential Utterances (NSUs) are short utterances that do not have the form of a full sentence but nevertheless convey a complete sentential meaning in the context of a conversation.NSUs are frequently used to ask follow up questions during interactions with question answer (QA) systems resulting into in-correct answers being presented to their users.Most of the current methods for resolving such NSUs have adopted rule or grammar based approach and have limited applicability.In this paper, we present a data driven statistical method for resolving such NSUs.Our method is based on the observation that humans identify keyword appearing in an NSU and place them in the context of conversation to construct a meaningful sentence.We adapt the keyword to question (K2Q) framework to generate natural language questions using keywords appearing in an NSU and its context.The resulting questions are ranked using different scoring methods in a statistical framework.Our evaluation on a data-set collected using mTurk shows that the proposed method perform significantly better than the previous work that has largely been rule based.